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Wilandari, Yuciana, Sri Haryatmi Kartiko, and Adhitya Ronnie Effendie. "ESTIMASI CADANGAN KLAIM MENGGUNAKAN GENERALIZED LINEAR MODEL (GLM) DAN COPULA." Jurnal Gaussian 9, no. 4 (December 7, 2020): 411–20. http://dx.doi.org/10.14710/j.gauss.v9i4.29260.
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In the articles of this will be discussed regarding the estimated reserves of the claim using the Generalized Linear Model (GLM) and Copula. Copula is a pair function distribution marginal becomes a function of distribution of multivariate. The use of copula regression in this article is to produce estimated reserves of claims. Generalized Linear Model (GLM) used as a marginal model for several lines of business. In research it is used three kinds of line of business that is individual, corporate and professional. The copula used is the Archimedean type of copula, namely Clayton and Gumbel copula. The best copula selection method is done using Akaike Information Criteria (AIC). Maximum Likelihood Estimation (MLE) is used to estimate copula parameters. The copula model used is the Clayton copula as the best copula. The parameter estimation results are used to obtain the estimated reserve value of the claim.
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Garrido, José, and Jun Zhou. "Full Credibility with Generalized Linear and Mixed Models." ASTIN Bulletin 39, no. 1 (May 2009): 61–80. http://dx.doi.org/10.2143/ast.39.1.2038056.
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AbstractGeneralized linear models (GLMs) are gaining popularity as a statistical analysis method for insurance data. For segmented portfolios, as in car insurance, the question of credibility arises naturally; how many observations are needed in a risk class before the GLM estimators can be considered credible? In this paper we study the limited fluctuations credibility of the GLM estimators as well as in the extended case of generalized linear mixed model (GLMMs). We show how credibility depends on the sample size, the distribution of covariates and the link function. This provides a mechanism to obtain confidence intervals for the GLM and GLMM estimators.
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Kafková, Silvie, and Lenka Křivánková. "Generalized Linear Models in Vehicle Insurance." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 62, no. 2 (2014): 383–88. http://dx.doi.org/10.11118/actaun201462020383.
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Actuaries in insurance companies try to find the best model for an estimation of insurance premium. It depends on many risk factors, e.g. the car characteristics and the profile of the driver. In this paper, an analysis of the portfolio of vehicle insurance data using a generalized linear model (GLM) is performed. The main advantage of the approach presented in this article is that the GLMs are not limited by inflexible preconditions. Our aim is to predict the relation of annual claim frequency on given risk factors. Based on a large real-world sample of data from 57 410 vehicles, the present study proposed a classification analysis approach that addresses the selection of predictor variables. The models with different predictor variables are compared by analysis of deviance and Akaike information criterion (AIC). Based on this comparison, the model for the best estimate of annual claim frequency is chosen. All statistical calculations are computed in R environment, which contains stats package with the function for the estimation of parameters of GLM and the function for analysis of deviation.
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Putra, Tri Andika Julia, Donny Citra Lesmana, and I. Gusti Putu Purnaba. "Penghitungan Premi Asuransi Kendaraan Bermotor Menggunakan Generalized Linear Models dengan Distribusi Tweedie." Jambura Journal of Mathematics 3, no. 2 (May 4, 2021): 115–27. http://dx.doi.org/10.34312/jjom.v3i2.10136.
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ABSTRAKSeorang aktuaris mempunyai tugas penting dalam menentukan harga premi yang sesuai untuk setiap nasabah dengan risiko dan karakteristik yang berbeda. Banyak variabel yang dapat mempengaruhi harga premi. Oleh karena itu, aktuaris harus mengetahui variabel-variabel yang berpengaruh signifikan terhadap premi. Tujuan dari penelitian ini adalah untuk menentukan variabel yang dapat mempengaruhi besaran premi murni menggunakan distribusi campuran dalam menentukan besarnya premi melalui Generalized Linear Models (GLM) serta menentukan model harga premi yang sesuai berdasarkan variabel-variabel yang mempengaruhinya. Salah satu analisis statistik yang dapat digunakan untuk memodelkan premi asuransi adalah Generalized Linear Models. GLM merupakan perluasan dari model regresi klasik yang dapat mengakomodasi fleksibilitas untuk menggunakan beberapa distribusi data tetapi terbatas pada distribusi keluarga eksponensial. Dalam model GLM, premi diperoleh dengan mengalikan nilai ekspektasi bersyarat dari frekuensi klaim dan biaya klaim. Berdasarkan penelitian yang telah dilakukan diketahui bahwa frekuensi klaim dan besarnya klaim mengikuti distribusi Tweedie. Dari kedua model tersebut diketahui bahwa variabel yang mempengaruhi premi murni adalah jumlah anak, pendapatan per bulan, status pernikahan, pendidikan, pekerjaan, penggunaan kendaraan, besarnya bluebook yang dibayarkan, dan jenis kendaraan nasabah. Hal ini menunjukkan bahwa model GLM merupakan model yang representatif dan berguna bagi perusahaan asuransi. ABSTRACTIt is an important task for an actuary in determining the appropriate premium price for each customer with different risks and characteristics. Many variables can affect the premium price. Therefore, actuaries must determine the variables that significantly affect the premium. The purpose of this study is to determine the variables that can affect the amount of pure premium using a mixed distribution in determining the amount of premium through Generalized Linear Models (GLM) and determine the appropriate premium price model based on the variables that influence it. One of the statistical analyzes that can be used to model insurance premiums is the Generalized Linear Models. GLM is an extension of the classic regression model that can accommodate the flexibility of its users to use multiple data distributions but is limited to the exponential family distribution. In the GLM model, the premium is obtained by multiplying the conditional expected value of the frequency of claims and the cost of claims. Based on the research that has been done, it is known that the frequency of claims and the size of claims follow the Tweedie distribution. From the two models, it is known that the variables affecting the pure premium are the number of children, monthly income, marital status, education, occupation, vehicle use, the number of bluebooks paid, and the type of vehicle from the customer. This shows that the GLM model is a representative and useful model for the insurance company business.
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Østergaard, Jacob, Mark A. Kramer, and Uri T. Eden. "Capturing Spike Variability in Noisy Izhikevich Neurons Using Point Process Generalized Linear Models." Neural Computation 30, no. 1 (January 2018): 125–48. http://dx.doi.org/10.1162/neco_a_01030.
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To understand neural activity, two broad categories of models exist: statistical and dynamical. While statistical models possess rigorous methods for parameter estimation and goodness-of-fit assessment, dynamical models provide mechanistic insight. In general, these two categories of models are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input current. We then fit these spike train data with a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured.
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Santi, Vera Maya, Abi Wiyono, and Sudarwanto. "Pemodelan Jumlah Kasus Malaria di Indonesia Menggunakan Generalized Linear Model." Jurnal Statistika dan Aplikasinya 5, no. 1 (June 30, 2021): 112–20. http://dx.doi.org/10.21009/jsa.05111.
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Generalized Linear Model (GLM) telah banyak digunakan untuk memodelkan berbagai macam tipe data dimana distribusi dari variabel respon merupakan distribusi yang termasuk dalam distribusi keluarga eksponensial. Contoh umum dari distribusi keluarga eksponensial adalah distribusi Poisson dan Binomial. Model regresi GLM mendeskripsikan struktur dari variabel prediktor, sedangkan fungsi penghubung secara khusus mendeskripsikan hubungan antara model regresi dengan nilai ekspektasi dari variabel respon. Tujuan dari artikel ini adalah mendapatkan variabel-variabel prediktor yang berpengaruh signifikan terhadap model. Metode Maximum Likelihood Estimation digunakan untuk mencari estimasi dari nilai parameter regresi model. Jumlah kasus malaria di Indonesia diidentifikasi berdistribusi Poisson. Terdapat 3 variabel prediktor yang berpengaruh signifikan terhadap jumlah kasus malaria di Indonesia, yaitu persentase rumah tangga yang memiliki akses sanitasi layak, jumlah kabupaten/kota yang menyelenggarakan tatanan kawasan kesehatan dan jumlah kabupaten/kota yang melakukan pengendalian vektor terpadu.
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Nogués-Bravo, D. "Comparing regression methods to predict species richness patterns." Web Ecology 9, no. 1 (December 9, 2009): 58–67. http://dx.doi.org/10.5194/we-9-58-2009.
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Abstract. Multivariable regression models have been used extensively as spatial modelling tools. However, other regression approaches are emerging as more efficient techniques. This paper attempts to present a synthesis of Generalised Regression Models (Generalized Linear Models, GLMs, Generalized Additive Models, GAMs), and a Geographically Weighted Regression, GWR, implemented in a GAM, explaining their statistical formulations and assessing improvements in predictive accuracy compared with linear regressions. The problems associated with these approaches are also discussed. A digital database developed with Geographic Information Systems (GIS), including environmental maps and bird species richness distribution in northern Spain, is used for comparison of the techniques. GWR using splines has shown the highest improvement in accounted deviance when compared with traditional linear regression approach, followed by GAM and GLM.
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Harini, K., and K. Sashi Rekha. "Comparison of Logistic Regression and Generalized Linear Model for Identifying Accurate At – Risk Students." Alinteri Journal of Agriculture Sciences 36, no. 1 (June 22, 2021): 399–405. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21060.
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Aim: To predict the accuracy percentage of At - risk students based on High withdrawal and Failure rate. Materials and methods: Logistic Regression with sample size = 20 and Generalised Linear Model (GLM) with sample size = 20 was iterated different times for predicting accuracy percentage of At - risk students. The Novel sigmoid function used in Logistic Regression maps prediction to probabilities which helps to improve the prediction of accuracy percentage. Results and Discussion: Logistic Regression has significantly better accuracy (94.48 %) compared to GLM accuracy (92.76 %). There was a statistical significance between Logistic regression and GLM (p=0.000) (p<0.05). Conclusion: Logistic Regression with Novel Sigmoid function helps in predicting with more accuracy percentage of At - risk students.
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Saavedra, Angeles, Javier Taboada, María Araújo, and Eduardo Giráldez. "Generalized Linear Spatial Models to Predict Slate Exploitability." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/531062.
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The aim of this research was to determine the variables that characterize slate exploitability and to model spatial distribution. A generalized linear spatial model (GLSMs) was fitted in order to explore relationship between exploitability and different explanatory variables that characterize slate quality. Modelling the influence of these variables and analysing the spatial distribution of the model residuals yielded a GLSM that allows slate exploitability to be predicted more effectively than when using generalized linear models (GLM), which do not take spatial dependence into account. Studying the residuals and comparing the prediction capacities of the two models lead us to conclude that the GLSM is more appropriate when the response variable presents spatial distribution.
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Ohigashi, Kentaro. "An introductory guide to the statistical analysis—from general linear model to the generalized linear model—." Journal of Weed Science and Technology 55, no. 4 (2010): 268–74. http://dx.doi.org/10.3719/weed.55.268.
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Gu, Guo-Xue, and Shang-Mei Zhao. "Risk Measuring Model on Public Liability Fire and Empirical Study in China." Journal of Disaster Research 9, no. 1 (February 1, 2014): 35–41. http://dx.doi.org/10.20965/jdr.2014.p0035.
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Public fire insurance has recently appeared in China. The basis for calculating the premium is the accurate measurement of Publicliability risk in fire. The generalized linear model (GLM) is widely used for measuring this risk in practice, but the GLM often cannot be satisfied, especially in fat-tailed distribution. A nonparametric Gaussian kernel linear model used to improve the GLM is applied to measure publicliability risk in fire, yielding a favorable effect. Results show three major risk factors that were measured precisely – the nature of the industry, the scale of public places and the level of fire precaution.
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Olanrewaju, Rasaki Olawale. "Integer-valued Time Series Model via Generalized Linear Models Technique of Estimation." International Annals of Science 4, no. 1 (April 29, 2018): 35–43. http://dx.doi.org/10.21467/ias.4.1.35-43.
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The paper authenticated the need for separate positive integer time series model(s). This was done from the standpoint of a proposal for both mixtures of continuous and discrete time series models. Positive integer time series data are time series data subjected to a number of events per constant interval of time that relatedly fits into the analogy of conditional mean and variance which depends on immediate past observations. This includes dependency among observations that can be best described by Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model with Poisson distributed error term due to its positive integer defined range of values. As a result, an integer GARCH model with Poisson distributed error term was formed in this paper and called Integer Generalized Autoregressive Conditional Heteroscedasticity (INGARCH). Iterative Reweighted Least Square (IRLS) parameter estimation technique type of the Generalized Linear Models (GLM) was adopted to estimate parameters of the two spilt models; Linear and Log-linear INGARCH models deduced from the identity link function and logarithmic link function, respectively. This resulted from the log-likelihood function generated from the GLM via the random component that follows a Poisson distribution. A study of monthly successful bids of auction from 2003 to 2015 was carried out. The Probabilistic Integral Transformation (PIT) and scoring rule pinpointed the uniformity of the linear INGARCH than that of the log-linear INGARCH in describing first order autocorrelation, serial dependence and positive conditional effects among covariates based on the immediate past. The linear INGARCH model outperformed the log-linear INGARCH model with (AIC = 10514.47, BIC = 10545.01, QIC = 34128.56) and (AIC = 37588.83, BIC = 37614.28, QIC = 37587.3), respectively.
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Zhou, Shijie, Robert A. Campbell, and Simon D. Hoyle. "Catch per unit effort standardization using spatio-temporal models for Australia’s Eastern Tuna and Billfish Fishery." ICES Journal of Marine Science 76, no. 6 (March 11, 2019): 1489–504. http://dx.doi.org/10.1093/icesjms/fsz034.
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Abstract The majority of catch per unit effort (cpue) standardizations use generalized linear models (GLMs) or generalized additive models (GAMs). We develop geostatistical models that model catch locations as continuous Gaussian random fields (GRFs) and apply them to standardizing cpue in Australia’s Eastern Tuna and Billfish Fishery (ETBF). The results are compared with the traditional GLMs currently used in ETBF assessments as well as GAMs. Specifically, we compare seven models in three groups: two GLMs, two GAMs, and three GRF models. Within each group, one model treats spatial and temporal variables independently, while the other model(s) treats them together as an interaction term. The two spatio-temporal GRF models differ in treating the spatial–temporal interaction: either as a random process or as an autoregressive process. We simulate catch rate data for five pelagic species based on real fishery catch rates so that the simulated data reflect real fishery patterns while the “true” annual abundance levels are known. The results show that within each group, the model with a spatial–temporal interaction term significantly outperforms the other model treating spatial and temporal variables independently. For spatial–temporal models between the three groups, prediction accuracy tends to improve from GLM to GAM and to the GRF models. Overall, the spatio-temporal GRF autoregressive model reduces mean relative predictive error by 43.4% from the GLM, 33.9% from the GAM, and reduces mean absolute predictive error by 23.5% from the GLM and 3.3% from the GAM, respectively. The comparison suggests a promising direction for further developing the geostatistical models for the ETBF.
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XU, SHIZHONG. "Testing Hardy–Weinberg disequilibrium using the generalized linear model." Genetics Research 94, no. 6 (December 2012): 319–30. http://dx.doi.org/10.1017/s0016672312000511.
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SummaryCurrent methods for detecting Hardy–Weinberg disequilibrium (HWD) only deal with one locus at a time. We developed a method that can jointly detect HWD for multiple loci. The method was developed under the generalized linear model (GLM) using the probit link function. When applied to a single locus, the new method is more powerful than the exact test. When applied to two or more loci, the method can reduce false positives caused by linkage disequilibrium (LD). We applied the method to 24 single nucleotide polymorphism (SNP) markers of a single human gene and eliminated several false positive HWDs due to LD. We developed an R package ‘hwdglm’ for joint HWD detection, which can be downloaded from our personal website (www.statgen.ucr.edu).
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Marzjarani, Morteza. "A Comparison of a General Linear Model and the Ratio Estimator." International Journal of Statistics and Probability 9, no. 3 (April 15, 2020): 54. http://dx.doi.org/10.5539/ijsp.v9n3p54.
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In data analysis, selecting a proper statistical model is a challenging issue. Upon the selection, there are other important factors impacting the results. In this article, two statistical models, a General Linear Model (GLM) and the Ratio Estimator will be compared. Where applicable, some issues such as heteroscedasticity, outliers, etc. and the role they play in data analysis will be studied. For reducing the severity of heteroscedasticity, Weighted Least Square (WLS), Generalized Least Square (GLS), and Feasible Generalized Least Square (FGLS) will be deployed. Also, a revised version of FGLS is introduced. Since these issues are data dependent, shrimp effort data collected in the Gulf of Mexico for the years 2005 through 2018 will be used and it is shown that the revised FGLS reduces the impact of heteroscedasticity significantly compared to that of FGLS. The data sets will also be checked for the outliers and corrections are made (where applicable). It is concluded that these issues play a significant role in data analysis and must be taken seriously. Further, the two statistical models, that is, the GLM and the Ratio Estimator are compared.
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Lal Shrestha, Srijan. "Particulate Air Pollution and Daily Mortality in Kathmandu Valley, Nepal: Associations and Distributed Lag." Open Atmospheric Science Journal 6, no. 1 (April 20, 2012): 62–70. http://dx.doi.org/10.2174/1874282301206010062.
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The distributed lag effect of ambient particulate air pollution that can be attributed to all cause mortality in Kathmandu valley, Nepal is estimated through generalized linear model (GLM) and generalized additive model (GAM) with autoregressive count dependent variable. Models are based upon daily time series data on mortality collected from the leading hospitals and exposure collected from the 6 six strategically dispersed fixed stations within the valley. The distributed lag effect is estimated by assigning appropriate weights governed by a mathematical model in which weights increased initially and decreased later forming a long tail. A comparative assessment revealed that autoregressive semiparametric GAM is a better fit compared to autoregressive GLM. Model fitting with autoregressive semi-parametric GAM showed that a 10 μg m rise in PM is associated with 2.57 % increase in all cause mortality accounted for 20 days lag effect which is about 2.3 times higher than observed for one day lag and demonstrates the existence of extended lag effect of ambient PM on all cause deaths. The confounding variables included in the model were parametric effects of seasonal differences measured by Fourier series terms, lag effect of mortality, and nonparametric effect of temperature represented by loess smoothing. The lag effects of ambient PM remained constant beyond 20 days.
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Wooldridge, Jeffrey M. "On the Limits of Glm for Specification Testing: A Comment on Gurmu and Trivedi." Econometric Theory 10, no. 2 (June 1994): 409–18. http://dx.doi.org/10.1017/s0266466600008471.
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In this comment on Gurmu and Trivedi's “Variable Augmentation Specification Tests in the Linear Exponential Family,” I show how their generalized linear model (GLM) approach relates to other work in econometrics on specification testing in the linear exponential family. In addition to shedding light on the relationship between the statistics and econometrics literatures on testing in quasi-likelihood frameworks, this comparison reveals some important limitations of GLM as a general framework for devising specification tests.
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Zhong, Yuan, Baoxin Hu, G. Brent Hall, Farah Hoque, Wei Xu, and Xin Gao. "A Generalized Linear Mixed Model Approach to Assess Emerald Ash Borer Diffusion." ISPRS International Journal of Geo-Information 9, no. 7 (June 27, 2020): 414. http://dx.doi.org/10.3390/ijgi9070414.
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The Asian Emerald Ash Borer beetle (EAB, Agrilus planipennis Fairmaire) can cause damage to all species of Ash trees (Fraxinus), and rampant, unchecked infestations of this insect can cause significant damage to forests. It is thus critical to assess and model the spread of the EAB in a manner that allows authorities to anticipate likely areas of future tree infestation. In this study, a generalized linear mixed model (GLMM), combining the features of the commonly used generalized linear model (GLM) and a random effects model, was developed to predict future EAB spread patterns in Southern Ontario, Canada. The GLMM was designed to deal with autocorrelation in the data. Two random effects were established based on the geographic information provided with the EAB data, and a method based on statistical inference was proposed to identify the most significant factors associated with the distribution of the EAB. The results of the model showed that 95% of the testing data were correctly classified. The predictive performance of the GLMM was substantially enhanced in comparison with that obtained by the GLM. The influence of climatic factors, such as wind speed and anthropogenic activities, had the most significant influence on the spread of the EAB.
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Little, Max A., Patrick E. McSharry, and James W. Taylor. "Generalized Linear Models for Site-Specific Density Forecasting of U.K. Daily Rainfall." Monthly Weather Review 137, no. 3 (March 1, 2009): 1029–45. http://dx.doi.org/10.1175/2008mwr2614.1.
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Abstract Site-specific probability density rainfall forecasts are needed to price insurance premiums, contracts, and other financial products based on precipitation. The spatiotemporal correlations in U.K. daily rainfall amounts over the Thames Valley are investigated and statistical Markov chain generalized linear models (Markov GLM) of rainfall are constructed. The authors compare point and density forecasts of total rainfall amounts, and forecasts of probability of occurrence of rain from these models and from other proposed density models, including persistence, statistical climatology, Markov chain, unconditional gamma and exponential mixture models, and density forecasts from GLM regression postprocessed NCEP numerical ensembles, at up to 45-day forecast horizons. The Markov GLMs and GLM processed ensembles produced skillful 1-day-ahead and short-term point forecasts. Diagnostic checks show all models are well calibrated, but GLMs perform best under the continuous-ranked probability score. For lead times of greater than 1 day, no models were better than the GLM processed ensembles at forecasting occurrence probability. Of all models, the ensembles are best able to account for the serial correlations in rainfall amounts. In conclusion, GLMs for future site-specific density forecasting are recommended. Investigations explain this conclusion in terms of the interaction between the autocorrelation properties of the data and the structure of the models tested.
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Prameswara, Laurentia Nindya Sari, Bambang Susanto, and Leopoldus Ricky Sasongko. "Pendekatan Generalized Linear Model Pada Regresi Kuantil Copula Normal Untuk Keterhubungan IHSG dan Kurs EUR-IDR." d'CARTESIAN 9, no. 2 (December 31, 2020): 97. http://dx.doi.org/10.35799/dc.9.2.2020.28263.
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Penelitian ini bertujuan untuk memperoleh estimasi parameter dan regresi kuantil pada suatu model distribusi bivariat yang disebut Copula sebagai alternatif regresi linier klasik dalam menganalisis keterhubungan dua peubah acak. Copula adalah model distribusi bivariat yang memiliki keunggulan selain karena tidak kaku terhadap asumsi distribusi tertentu, juga dapat menyatakan keterhubungan nonlinier. Copula yang dianalisis pada penelitian ini adalah Copula Normal. Sedangkan Generalized Linear Model (GLM) adalah perluasan dari model regresi linier klasik, yang salah satu komponen utamanya adalah fungsi link. Didapati bahwa regresi kuantil pada copula Normal merupakan suatu bentuk GLM dengan fungsi invers link yaitu . Regresi kuantil dan parameter copula Normal diestimasi dengan pendekatan GLM menggunakan metode Least Square. Estimasi regresi kuantil terbaik dilakukan dengan menghitung Mean Square Error (MSE). Validasi parameter copula dilakukan melalui simulasi dengan parametric bootstrap. Data yang digunakan dalam penelitian ini adalah data return IHSG sebagai peubah tak bebas dan data return kurs beli EUR-IDR sebagai peubah bebas. Hasil penelitian menunjukkan bahwa keterhubungan IHSG dan kurs beli EUR-IDR lemah dan tidak linier.
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UDDIN, MD NAZIR, and MUNNI BEGUM. "A generalized linear model for multivariate correlated binary response data on mobility index." Journal of Statistical Research 52, no. 1 (September 2, 2018): 61–73. http://dx.doi.org/10.47302/jsr.2018520104.
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Dependence in multivariate binary outcomes in longitudinal data is a challenging and an important issue to address. Numerous studies have been performed to test the dependence in binary responses either using conditional or marginal probability models. Since the con- ditional and marginal approach provide inadequate or misleading results, the joint models based on both are implemented for bivariate correlated binary responses. In the current paper, we consider a joint modeling approach and a generalized linear model (GLM) for tri-variate correlated binary responses. The link function of the GLM is used to test the dependence of response variables. The mobility index with two categories, no difficulty and difficulty, over the duration of three waves of Health and Retirement Survey (HRS) is chosen as the binary response variable. Initial analysis with Marshall-Olkin correlation coefficients and logistic regression coefficients provide moderate correlation in mobility indices implying dependence in the response variables. We also found statistically significant dependence among the response variables using the joint modeling approach. The mobility at current wave not only depends on the previous mobility status, but also depends on covariates such as age, gender, and race.
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Situmorang, Egidius Saut Poltak, Bambang Susanto, and Leopoldus Ricky Sasongko. "Estimasi Parameter Copula Plackett Untuk Data Bivariat Melalui Metode Generalized Linear Model Pada Regresi Mediannya." d'CARTESIAN 9, no. 2 (December 31, 2020): 105. http://dx.doi.org/10.35799/dc.9.2.2020.28264.
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Hubungan antardua peubah acak dapat dilakukan melalui pendekatan regresi linier. Namun keterbatasan regresi linier dalam pemenuhan asumsi klasik sering menjadi kendala analisis. Keterbatasan ini dapat diatasi dengan melibatkan model distribusi bivariat yang disebut copula pada analisis regresi. Copula memiliki keunggulan salah satunya adalah mampu menunjukkan keterhubungan yang tidak linier. Generalized Linear Model (GLM) adalah bentuk perluasan regresi linier. Diketahui bahwa regresi kuantil pada Copula Plackett merupakan suatu bentuk GLM dengan suatu fungsi invers link . Penelitian ini bertujuan untuk menganalisis keterhubungan dua peubah melalui parameter Copula Plackett yang diestimasi melalui pendekatan Generalized Linier Model pada regresi mediannya dengan metode Least Square. Validasi parameter Copula Plackett dilakukan dengan metode simulasi parametric bootstrap melalui pengulangan metode bagi dua. Regresi median terbaik dipilih melalui nilai Mean Square Error terkecil. Perolehan parameter Copula Plackett diterapkan pada data penelitian, yaitu return IHSG dan return kurs beli JPY-IDR untuk menganalisis keterhubungan keduanya. Hasil penelitian menunjukkan bahwa keterhubungan return IHSG dan return kurs beli JPY-IDR dinyatakan ada namun tidak dapat dikatakan saling bebas.
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Fox, Jean-Paul, Duco Veen, and Konrad Klotzke. "Generalized Linear Mixed Models for Randomized Responses." Methodology 15, no. 1 (January 1, 2019): 1–18. http://dx.doi.org/10.1027/1614-2241/a000153.
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Abstract. Response bias (nonresponse and social desirability bias) is one of the main concerns when asking sensitive questions about behavior and attitudes. Self-reports on sensitive issues as in health research (e.g., drug and alcohol abuse), and social and behavioral sciences (e.g., attitudes against refugees, academic cheating) can be expected to be subject to considerable misreporting. To diminish misreporting on self-reports, indirect questioning techniques have been proposed such as the randomized response techniques. The randomized response techniques avoid a direct link between individual’s response and the sensitive question, thereby protecting the individual’s privacy. Next to the development of the innovative data collection methods, methodological advances have been made to enable a multivariate analysis to relate responses to sensitive questions to other variables. It is shown that the developments can be represented by a general response probability model (including all common designs) by extending it to a generalized linear model (GLM) or a generalized linear mixed model (GLMM). The general methodology is based on modifying common link functions to relate a linear predictor to the randomized response. This approach makes it possible to use existing software for GLMs and GLMMs to model randomized response data. The R-package GLMMRR makes the advanced methodology available to applied researchers. The extended models and software will seriously improve the application of the randomized response methodology. Three empirical examples are given to illustrate the methods.
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Osawa, Takeshi, Hiromune Mitsuhashi, Yuta Uematsu, and Atushi Ushimaru. "Bagging GLM: Improved generalized linear model for the analysis of zero-inflated data." Ecological Informatics 6, no. 5 (September 2011): 270–75. http://dx.doi.org/10.1016/j.ecoinf.2011.05.003.
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Cui, James. "QIC Program and Model Selection in GEE Analyses." Stata Journal: Promoting communications on statistics and Stata 7, no. 2 (June 2007): 209–20. http://dx.doi.org/10.1177/1536867x0700700205.
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The generalized estimating equation (GEE) approach is a widely used statistical method in the analysis of longitudinal data in clinical and epidemiological studies. It is an extension of the generalized linear model (GLM) method to correlated data such that valid standard errors of the parameter estimates can be drawn. Unlike the GLM method, which is based on the maximum likelihood theory for independent observations, the gee method is based on the quasilikelihood theory and no assumption is made about the distribution of response observations. Therefore, Akaike's information criterion, a widely used method for model selection in glm, is not applicable to gee directly. However, Pan (Biometrics 2001; 57: 120–125) proposed a model-selection method for gee and termed it quasilikelihood under the independence model criterion. This criterion can also be used to select the best-working correlation structure. From Pan's methods, I developed a general Stata program, qic, that accommodates all the distribution and link functions and correlation structures available in Stata version 9. In this paper, I introduce this program and demonstrate how to use it to select the best working correlation structure and the best subset of covariates through two examples in longitudinal studies.
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Sudarwanto, S., L. Ambarwati, and I. Hadi. "Development of rental property insurance models with Generalized Linear Models (GLM)." Journal of Physics: Conference Series 1402 (December 2019): 077104. http://dx.doi.org/10.1088/1742-6596/1402/7/077104.
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Marazzi, Alfio. "Improving the Efficiency of Robust Estimators for the Generalized Linear Model." Stats 4, no. 1 (February 4, 2021): 88–107. http://dx.doi.org/10.3390/stats4010008.
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The distance constrained maximum likelihood procedure (DCML) optimally combines a robust estimator with the maximum likelihood estimator with the purpose of improving its small sample efficiency while preserving a good robustness level. It has been published for the linear model and is now extended to the GLM. Monte Carlo experiments are used to explore the performance of this extension in the Poisson regression case. Several published robust candidates for the DCML are compared; the modified conditional maximum likelihood estimator starting with a very robust minimum density power divergence estimator is selected as the best candidate. It is shown empirically that the DCML remarkably improves its small sample efficiency without loss of robustness. An example using real hospital length of stay data fitted by the negative binomial regression model is discussed.
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Cerruti, Brian J., and Steven G. Decker. "A Statistical Forecast Model of Weather-Related Damage to a Major Electric Utility." Journal of Applied Meteorology and Climatology 51, no. 2 (February 2011): 191–204. http://dx.doi.org/10.1175/jamc-d-11-09.1.
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AbstractA generalized linear model (GLM) has been developed to relate meteorological conditions to damages incurred by the outdoor electrical equipment of Public Service Electric and Gas, the largest public utility in New Jersey. Utilizing a perfect-prognosis approach, the model consists of equations derived from a backward-eliminated multiple-linear-regression analysis of observed electrical equipment damage as the predictand and corresponding surface observations from a variety of sources including local storm reports as the predictors. Weather modes, defined objectively by surface observations, provided stratification of the data and served to increase correlations between the predictand and predictors. The resulting regression equations produced coefficients of determination up to 0.855, with the lowest values for the heat and cold modes, and the highest values for the thunderstorm and mix modes. The appropriate GLM equations were applied to an independent dataset for model validation, and the GLM shows skill [i.e., Heidke skill score (HSS) values greater than 0] at predicting various thresholds of total accumulated equipment damage. The GLM shows higher HSS values relative to a climatological approach and a baseline regression model. Two case studies analyzed to critique model performance yielded insight into GLM shortcomings, with lightning information and wind duration being found to be important missing predictors under certain circumstances.
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Rosenlund, Stig. "Dispersion Estimates for Poisson and Tweedie Models." ASTIN Bulletin 40, no. 1 (May 2010): 271–79. http://dx.doi.org/10.2143/ast.40.1.2049229.
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AbstractAs a consequence of pointing out an ambiguity in Renshaw (1994), we show that the Overdispersed Poisson model cannot be generated by random independent intensities. Hence Pearson's chi-square-based estimate is normally unsuitable for GLM (Generalized Linear Model) log link claim frequency analysis in insurance. We propose a new dispersion parameter estimate in the GLM Tweedie model for risk premium. This is better than the Pearson estimate, if there are sufficiently many claims in each tariff cell. Simulation results are given showing the differences between it and the Pearson estimate.
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Guan, Weihua, and Roberto G. Gutierrez. "Programmable GLM: Two User-defined Links." Stata Journal: Promoting communications on statistics and Stata 2, no. 4 (December 2002): 378–90. http://dx.doi.org/10.1177/1536867x0200200404.
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With the release of Stata 7, the glm command for fitting generalized linear models underwent a substantial overhaul. Stata 7 glm contains an expanded array of variance estimators, regression diagnostics, and other enhancements. The overhaul took place to coincide with the release of Hardin and Hilbe (2001). With the new glm came a modular design that enables users to program customized link functions, variance functions, and weight functions to be used if Newey–West covariance estimates are desired. Because cases requiring customized link functions are the more prevalent in the literature, only those are considered here. We give two examples where a nonstandard link function is required: the relative survival model of Hakulinen and Tenkanen (1987)and a logistic model that accounts for natural response as described in Collett (2003). The relative ease (over previous versions of Stata) with which these alternate links can be programmed into glm is demonstrated.
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Omerašević, Amela, and Jasmina Selimović. "Classification Ratemaking Using Decision Tree in the Insurance Market of Bosnia and Herzegovina." South East European Journal of Economics and Business 15, no. 2 (December 1, 2020): 124–39. http://dx.doi.org/10.2478/jeb-2020-0020.
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Abstract This paper investigates the impact of risk classification on life insurance ratemaking with particular reference to Bosnia and Herzegovina (BiH). The research is based on a sample of over eighteen thousand insurance policies for passenger vehicles collected over the period 2015-2020. In our empirical investigation we develop a standard risk model based on the application of Poisson Generalized linear models (GLM) for claims frequency estimate and Gamma GLM for claim severity estimate. The analysis reveals that GLM does not provide a reliable parameter estimates for Multi-level factor (MLF) categorical predictors. Although GLM is widely used method to deter insurance premiums, improvements of GLM by using the data mining methods identified in this paper may solve practical challenges for the risk models. The popularity of applying data mining methods in the actuarial community has been growing in recent years due to its efficiency and precision. These models are recommended to be considered in BiH and South East European region in general.
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Khedhaouiria, Dikra, Alain Mailhot, and Anne-Catherine Favre. "Daily Precipitation Fields Modeling across the Great Lakes Region (Canada) by Using the CFSR Reanalysis." Journal of Applied Meteorology and Climatology 57, no. 10 (October 2018): 2419–38. http://dx.doi.org/10.1175/jamc-d-18-0019.1.
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AbstractReanalyses, generated by numerical weather prediction methods assimilating past observations, provide consistent and continuous meteorological fields for a specific period. In regard to precipitation, reanalyses cannot be used as a climate proxy of the observed precipitation, as biases and scale mismatches exist between the datasets. In the present study, a stochastic model output statistics (SMOS) approach combined with meta-Gaussian spatiotemporal random fields was employed to cope with these caveats. The SMOS is based on the generalized linear model (GLM) and the vector generalized linear model (VGLM) frameworks to model the precipitation occurrence and intensity, respectively. Both models use the Climate Forecast System Reanalysis (CFSR) precipitation as covariate and were locally calibrated at 173 sites across the Great Lakes region. Combined with meta-Gaussian random fields, the GLM and VGLM models allowed for the generation of spatially coherent daily precipitation fields across the region. The results indicated that the approach corrected systematic biases and provided an accurate spatiotemporal structure of daily precipitation. Performances of selected precipitation indicators from the joint Commission for Climatology (CCl)/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) were good and were systematically improved when compared to CFSR.
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Slam, Hajra, and Yasar Mahmood. "Contributory Factors of Traffic Accidents in Lahore Using Generalized Linear Models (GLM)." NUST Journal of Engineering Sciences 12, no. 1 (June 1, 2019): 33–43. http://dx.doi.org/10.24949/njes.v12i1.506.
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The geometric design of roads is the branch of highway engineering concerned with the positioning of the physical elements of the roadway according to standards and limitations with objectives to optimize efficiency and safety while minimizing cost and environmental damage. The present study aims to explore geometric design and other factors which cause of accidents in Lahore. Data is collected from TEPA (Traffic Engineering and Planning Agency), NESPAK (National Engineering Services Pakistan), CTP (City Traffic Police) and Rescue 1122 over a period of 3 years. Two phase sampling technique has been used. Data is carried out about demographic information, physical characteristics and geometric design of roads. All registered 356 traffic accidents have been used on Ferozpur Road, Multan Road, Canal Bank Road and Grand Trunk in Lahore. Poisson regression and negative binomial regression are discussed in this research. SPSS and R Language are used for analysis. The results show that most of accidents occur at office off timing and fatal due to reckless driving and over speeding. Mostly, cars and tralala hit the bikes and Pedestrians. The Poisson regression model gives good description of number of accidents depends on various explanatory variables. Number of lanes, type of locations and roadway light are statistically significant. Narrow Shoulder width (m), Median Width (m) and Lane width (m) increase accident occurrence. Three lanes and larger road structures increase accidents. Numbers of accident increase when Roadway, type of locations, roadway light and traffic control signals decrease.
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Carlos-Júnior, Lélis A., Joel C. Creed, Rob Marrs, Rob J. Lewis, Timothy P. Moulton, Rafael Feijó-Lima, and Matthew Spencer. "Generalized Linear Models outperform commonly used canonical analysis in estimating spatial structure of presence/absence data." PeerJ 8 (September 3, 2020): e9777. http://dx.doi.org/10.7717/peerj.9777.
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Background Ecological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs. Methods We simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure. Results Overall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA’s error rates under almost all scenarios. Conclusion We encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.
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Abdellatif, M., W. Atherton, and R. Alkhaddar. "A hybrid generalised linear and Levenberg–Marquardt artificial neural network approach for downscaling future rainfall in North Western England." Hydrology Research 44, no. 6 (January 16, 2013): 1084–101. http://dx.doi.org/10.2166/nh.2013.045.
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This paper describes a novel technique for downscaling daily rainfall which uses a combination of a generalised linear model (GLM) and artificial neural network (ANN) to downscale rainfall. A two-stage process is applied, an occurrence process which uses the GLM model and an amount process which uses an ANN model trained with a Levenberg–Marquardt approach. The GLM-ANN was compared with other three downscaling models, the traditional neural network (ANN), multiple linear regression (MLR) and Poisson regression (PR). The models are applied for downscaling daily rainfall at three locations in the North West of England during the winter and summer. Model performances with respect to reproduction of various statistics such as correlation coefficient, autocorrelation, root mean square errors (RMSE), standard deviation and the mean rainfall are examined. It is found that the GLM-ANN model performs better than the other three models in reproducing most daily rainfall statistics, with slight difficulties in predicting extremes rainfall event in summer. The GLM-ANN model is then used to project future rainfall at the three locations employing three different general circulation models (GCMs) for SRES scenarios A2 and B2. The study projects significant increases in mean daily rainfall at most locations for winter and decreases in summer.
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Mora, Freddy, Letícia de Menezes Gonçalves, Carlos Alberto Scapim, Elias Nunes Martins, and Maria de Fátima Pires da Silva Machado. "Generalized lineal models for the analysis of binary data from propagation experiments of Brazilian orchids." Brazilian Archives of Biology and Technology 51, no. 5 (October 2008): 963–70. http://dx.doi.org/10.1590/s1516-89132008000500013.
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This study aimed at applying the generalized linear models (GLM) for the analysis of a germination experiment of Cattleya bicolor in which the response variable was binary. The purpose of this experiment was to assess the effects of the storage temperatures and culture mediums on the seed viability. The analyses of variance was also carried out either with or without the data transformation. All the statistical approaches indicated the importance of the storage temperature on the seed viability. But, the culture media and interaction effects were significant only by the GLM. Based on the GLM, the seeds stored at 10°C increased viability, in which the coconut medium achieved the best performance. The results emphasized the importance of adopting the GLM to improve the reliability in many situations where the response variable followed a non-normal distribution.
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Bry, Xavier, Catherine Trottier, Frédéric Mortier, and Guillaume Cornu. "Component-based regularization of a multivariate GLM with a thematic partitioning of the explanatory variables." Statistical Modelling 20, no. 1 (December 12, 2018): 96–119. http://dx.doi.org/10.1177/1471082x18810114.
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We address component-based regularization of a multivariate generalized linear model (GLM). A vector of random responses [Formula: see text] is assumed to depend, through a GLM, on a set [Formula: see text] of explanatory variables, as well as on a set [Formula: see text] of additional covariates. [Formula: see text] is partitioned into [Formula: see text] conceptually homogenous variable groups [Formula: see text], viewed as explanatory themes. Variables in each [Formula: see text] are assumed many and redundant. Thus, generalized linear regression demands dimension reduction and regularization with respect to each [Formula: see text]. By contrast, variables in [Formula: see text] are assumed few and selected so as to demand no regularization. Regularization is performed searching each [Formula: see text] for an appropriate number of orthogonal components that both contribute to model [Formula: see text] and capture relevant structural information in [Formula: see text]. To estimate a single-theme model, we first propose an enhanced version of Supervised Component Generalized Linear Regression (SCGLR), based on a flexible measure of structural relevance of components, and able to deal with mixed-type explanatory variables. Then, to estimate the multiple-theme model, we develop an algorithm encapsulating this enhanced SCGLR: THEME-SCGLR. The method is tested on simulated data and then applied to rainforest data in order to model the abundance of tree species.
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Yee Peng, Loo, Habshah Midi, Sohel Rana, and Anwar Fitrianto. "Identification of Multiple Outliers in a Generalized Linear Model with Continuous Variables." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/5840523.
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In the statistical analysis of data, a model might be awfully fitted with the presence of outliers. Besides, it has been well established to use residuals for identification of outliers. The asymptotic properties of residuals can be utilized to contribute diagnostic tools. However, it is now evident that most of the existing diagnostic methods have failed in identifying multiple outliers. Therefore, this paper proposed a diagnostic method for the identification of multiple outliers in GLM, where traditionally used outlier detection methods are effortless as they undergo masking or swamping dilemma. Hence, an investigation was carried out to determine the capability of the proposed GSCPR method. The findings obtained from the numerical examples indicated that the performance of the proposed method was satisfactory for the identification of multiple outliers. Meanwhile, in the simulation study, two scenarios were considered to assess the validity of the proposed method. The proposed method consistently displayed higher percentage of correct detection, as well as lower rates of swamping and masking, regardless of the sample size and the contamination levels.
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Langley, Adam, Karine Briand, David Seán Kirby, and Raghu Murtugudde. "Influence of oceanographic variability on recruitment of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean." Canadian Journal of Fisheries and Aquatic Sciences 66, no. 9 (September 2009): 1462–77. http://dx.doi.org/10.1139/f09-096.
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Recruitment estimates for yellowfin tuna ( Thunnus albacares ) in the western and central Pacific Ocean (WCPO), derived from a stock assessment model, are highly variable seasonally, interannually, and over decadal periods. A generalized linear model (GLM) was developed that predicts the variation in yellowfin tuna recruitment in response to a range of oceanographic variables. The GLM model accounted for 54% of the variation in quarterly recruitment for the period 1980–2003, with the inclusion of seven different oceanographic variables derived from a zone within the northwestern equatorial region of the WCPO. The robustness of the recruitment model was investigated by cross-validation. The GLM was complemented by a cluster analysis approach that identified five principal oceanographic states within the northwestern zone selected by the GLM. Incorporation of the recent GLM recruitment indices in the yellowfin tuna stock assessment model is likely to improve the precision of estimates of current and projected (next 1–2 years) biomass and exploitation rates. In a broader context, the recruitment model provides a tool to investigate how yellowfin tuna recruitment might vary in response to short- and long-term variation in the oceanographic conditions of the WCPO.
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Fortin, Mathieu. "Population-averaged predictions with generalized linear mixed-effects models in forestry: an estimator based on Gauss−Hermite quadrature." Canadian Journal of Forest Research 43, no. 2 (February 2013): 129–38. http://dx.doi.org/10.1139/cjfr-2012-0268.
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Data in forestry are often spatially and (or) serially correlated. In the last two decades, mixed models have become increasingly popular for the analysis of such data because they can relax the assumption of independent observations. However, when the relationship between the response variable and the covariates is nonlinear, as is the case in generalized linear mixed models (GLMMs), population-averaged predictions cannot be obtained from the fixed effects alone. This study proposes an estimator, which is based on a five-point Gauss−Hermite quadrature, for population-averaged predictions in the context of GLMM. The estimator was tested through Monte Carlo simulation and compared with a regular generalized linear model (GLM). The estimator was also applied to a real-world case study, a harvest model. The results showed that GLM predictions were unbiased but that their confidence intervals did not achieve their nominal coverage. On the other hand, the proposed estimator yielded unbiased predictions with reliable confidence intervals. The predictions based on the fixed effects of a GLMM exhibited the largest biases. If statistical inferences are needed, the proposed estimator should be used. It is easily implemented as long as the random effect specification does not contain multiple random effects for the same hierarchical level.
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Wang, Wanjie, Shreejoy J. Tripathy, Krishnan Padmanabhan, Nathaniel N. Urban, and Robert E. Kass. "An Empirical Model for Reliable Spiking Activity." Neural Computation 27, no. 8 (August 2015): 1609–23. http://dx.doi.org/10.1162/neco_a_00754.
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Understanding a neuron’s transfer function, which relates a neuron’s inputs to its outputs, is essential for understanding the computational role of single neurons. Recently, statistical models, based on point processes and using generalized linear model (GLM) technology, have been widely applied to predict dynamic neuronal transfer functions. However, the standard version of these models fails to capture important features of neural activity, such as responses to stimuli that elicit highly reliable trial-to-trial spiking. Here, we consider a generalization of the usual GLM that incorporates nonlinearity by modeling reliable and nonreliable spikes as being generated by distinct stimulus features. We develop and apply these models to spike trains from olfactory bulb mitral cells recorded in vitro. We find that spike generation in these neurons is better modeled when reliable and unreliable spikes are considered separately and that this effect is most pronounced for neurons with a large number of both reliable and unreliable spikes.
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Silveira, T. C. L., A. M. S. Gama, T. P. Alves, and N. F. Fontoura. "Modeling habitat suitability of the invasive clam Corbicula fluminea in a Neotropical shallow lagoon, southern Brazil." Brazilian Journal of Biology 76, no. 3 (April 19, 2016): 718–25. http://dx.doi.org/10.1590/1519-6984.01915.
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Abstract This study aimed to model the habitat suitability for an invasive clam Corbicula fluminea in a coastal shallow lagoon in the southern Neotropical region (–30.22, –50.55). The lagoon (19km2, maximum deep 2.5m) was sampled with an Ekman dredge in an orthogonal matrix comprising 84 points. At each sampling point, were obtained environmental descriptors as depth, organic matter content (OMC), average granulometry (Avgran), and the percentage of sand (Pcsand). Prediction performance of Generalized Linear Models (GLM), Generalized Additive Models (GAM) and Boosted Regression Tree (BRT) were compared. Also, niche overlapping with other native clam species (Castalia martensi, Neocorbicula limosa and Anodontites trapesialis) was examined. A BRT model with 1400 trees was selected as the best model, with cross-validated correlation of 0.82. The relative contributions of predictors were Pcsand-42.6%, OMC-35.8%, Avgran-10.9% and Depth-10.8%. Were identified that C. fluminea occur mainly in sandy sediments with few organic matter, in shallow areas nor by the shore. The PCA showed a wide niche overlap with the native clam species C. martensi, N. limosa and A. trapesialis.
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Abuhelwa, Ahmad Y., Ganessan Kichenadasse, Ross A. McKinnon, Andrew Rowland, Ashley M. Hopkins, and Michael J. Sorich. "Machine Learning for Prediction of Survival Outcomes with Immune-Checkpoint Inhibitors in Urothelial Cancer." Cancers 13, no. 9 (April 21, 2021): 2001. http://dx.doi.org/10.3390/cancers13092001.
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Machine learning (ML) may enhance the efficiency of developing accurate prediction models for survival, which is critical in informing disease prognosis and care planning. This study aimed to develop an ML prediction model for survival outcomes in patients with urothelial cancer-initiating atezolizumab and to compare model performances when built using an expert-selected (curated) versus an all-in list (uncurated) of variables. Gradient-boosted machine (GBM), random forest, Cox-boosted, and penalised, generalised linear models (GLM) were evaluated for predicting overall survival (OS) and progression-free survival (PFS) outcomes. C-statistic (c) was utilised to evaluate model performance. The atezolizumab cohort in IMvigor210 was used for model training, and IMvigor211 was used for external model validation. The curated list consisted of 23 pretreatment factors, while the all-in list consisted of 75. Using the best-performing model, patients were stratified into risk tertiles. Kaplan–Meier analysis was used to estimate survival probabilities. On external validation, the curated list GBM model provided slightly higher OS discrimination (c = 0.71) than that of the random forest (c = 0.70), CoxBoost (c = 0.70), and GLM (c = 0.69) models. All models were equivalent in predicting PFS (c = 0.62). Expansion to the uncurated list was associated with worse OS discrimination (GBM c = 0.70; random forest c = 0.69; CoxBoost c = 0.69, and GLM c = 0.69). In the atezolizumab IMvigor211 cohort, the curated list GBM model discriminated 1-year OS probabilities for the low-, intermediate-, and high-risk groups at 66%, 40%, and 12%, respectively. The ML model discriminated urothelial-cancer patients with distinctly different survival risks, with the GBM applied to a curated list attaining the highest performance. Expansion to an all-in approach may harm model performance.
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Shen, Bo-Wen. "Aggregated Negative Feedback in a Generalized Lorenz Model." International Journal of Bifurcation and Chaos 29, no. 03 (March 2019): 1950037. http://dx.doi.org/10.1142/s0218127419500378.
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In this study, we first present a generalized Lorenz model (LM) with [Formula: see text] modes, where [Formula: see text] is an odd number that is greater than three. The generalized LM (GLM) is derived based on a successive extension of the nonlinear feedback loop (NFL) with additional high wavenumber modes. By performing a linear stability analysis with [Formula: see text] and [Formula: see text], we illustrate that: (1) within the 3D, 5D, and 7D LMs, the appearance of unstable nontrivial critical points requires a larger Rayleigh parameter in a higher-dimensional LM and (2) within the 9DLM, nontrivial critical points are stable. By comparing the GLM with various numbers of modes, we discuss the aggregated negative feedback enabled by the extended NFL and its role in stabilizing solutions in high-dimensional LMs. Our analysis indicates that the 9DLM is the lowest order generalized LM with stable nontrivial critical points for all Rayleigh parameters greater than one. As shown by calculations of the ensemble Lyapunov exponent, the 9DLM still produces chaotic solutions. Within the 9DLM, a larger critical value for the Rayleigh parameter, [Formula: see text], is required for the onset of chaos as compared to a [Formula: see text] for the 3DLM, a [Formula: see text] for the 5DLM, and a [Formula: see text] for the 7DLM. In association with stable nontrivial critical points that may lead to steady-state solutions, the appearance of chaotic orbits indicates the important role of a saddle point at the origin in producing the sensitive dependence of solutions on initial conditions. The 9DLM displays the coexistence of chaotic and steady-state solutions at moderate Rayleigh parameters and the coexistence of limit cycle and steady-state solutions at large Rayleigh parameters. The first kind of coexistence appears within a smaller range of Rayleigh parameters in lower-dimensional LMs (i.e. [Formula: see text] within the 3DLM) but in a wider range of Rayleigh parameters within the 9DLM (i.e. [Formula: see text]). The second kind of coexistence has never been reported in high-dimensional Lorenz systems.
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Lawal, Bayo H. "GLM for Some Class of Com-Poisson Distributions with Applications." International Journal of Statistics and Probability 7, no. 6 (August 17, 2018): 1. http://dx.doi.org/10.5539/ijsp.v7n6p1.
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In this paper, we present regression models (GLM) for the class of Conway-Maxwell-Poisson (Com-Poisson) distributions. This class of models include the Com-Poisson, the Com-Poisson negative binomial, the Generalized Com-Poisson and the Extended Com-Poisson distributions, all of which have been presented in various literatures within the last five years. While these distributions have been applied most especially to frequency count data exhibiting over or under dispersion, not much has been presented in the application of this class of models to data having several covariates (the exception being the Com-Poisson itself). Thus in this paper, we present the generalized linear model formulation for these distributions and compare our results with the baseline Com-Poisson and Poisson models. Two data sets are employed in this application. We further extended our discussion to the zero-inflated versions of these distributions and applying same to a well established data with having 64\% zero observations. All the models are fitted using SAS PROC NLMIXED. In all cases, empirical means and variances are generated which leads to our ability to compute the Wald's goodness-of-fit test statistic for all the models employed in this paper.
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Zhu, Rui, Chao Jiang, Xiaofeng Wang, Shuang Wang, Hao Zheng, and Haixu Tang. "Privacy-preserving construction of generalized linear mixed model for biomedical computation." Bioinformatics 36, Supplement_1 (July 1, 2020): i128—i135. http://dx.doi.org/10.1093/bioinformatics/btaa478.
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Abstract Motivation The generalized linear mixed model (GLMM) is an extension of the generalized linear model (GLM) in which the linear predictor takes random effects into account. Given its power of precisely modeling the mixed effects from multiple sources of random variations, the method has been widely used in biomedical computation, for instance in the genome-wide association studies (GWASs) that aim to detect genetic variance significantly associated with phenotypes such as human diseases. Collaborative GWAS on large cohorts of patients across multiple institutions is often impeded by the privacy concerns of sharing personal genomic and other health data. To address such concerns, we present in this paper a privacy-preserving Expectation–Maximization (EM) algorithm to build GLMM collaboratively when input data are distributed to multiple participating parties and cannot be transferred to a central server. We assume that the data are horizontally partitioned among participating parties: i.e. each party holds a subset of records (including observational values of fixed effect variables and their corresponding outcome), and for all records, the outcome is regulated by the same set of known fixed effects and random effects. Results Our collaborative EM algorithm is mathematically equivalent to the original EM algorithm commonly used in GLMM construction. The algorithm also runs efficiently when tested on simulated and real human genomic data, and thus can be practically used for privacy-preserving GLMM construction. We implemented the algorithm for collaborative GLMM (cGLMM) construction in R. The data communication was implemented using the rsocket package. Availability and implementation The software is released in open source at https://github.com/huthvincent/cGLMM. Supplementary information Supplementary data are available at Bioinformatics online.
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Lu, Pan, Hao Wang, and Denver Tolliver. "Prediction of Bridge Component Ratings Using Ordinal Logistic Regression Model." Mathematical Problems in Engineering 2019 (April 16, 2019): 1–11. http://dx.doi.org/10.1155/2019/9797584.
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Prediction of bridge component condition is fundamental for well-informed decisions regarding the maintenance, repair, and rehabilitation (MRR) of highway bridges. The National Bridge Inventory (NBI) condition rating is a major source of bridge condition data in the United States. In this study, a type of generalized linear model (GLM), the ordinal logistic statistical model, is presented and compared with the traditional regression model. The proposed model is evaluated in terms of reliability (the ability of a model to accurately predict bridge component ratings or the agreement between predictions and actual observations) and model fitness. Five criteria were used for evaluation and comparison: prediction error, bias, accuracy, out-of-range forecasts, Akaike’s Information Criteria (AIC), and log likelihood (LL). In this study, an external validation procedure was developed to quantitatively compare the forecasting power of the models for highway bridge component deterioration. The GLM method described in this study allows modeling ordinal and categorical dependent variable and shows slightly but significantly better model fitness and prediction performance than traditional regression model.
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Chun, K. P., H. S. Wheater, and C. J. Onof. "Streamflow estimation for six UK catchments under future climate scenarios." Hydrology Research 40, no. 2-3 (April 1, 2009): 96–112. http://dx.doi.org/10.2166/nh.2009.086.
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Possible changes in streamflow in response to climate variation are crucial for anthropological and ecological systems. However, estimates of precipitation under future climate scenarios are notoriously uncertain. In this article, rainfall time series are generated by the generalized linear model (GLM) approach in which stochastic time series are generated using alternative climate model output variables and potential evaporation series estimated by a temperature method. These have been input to a conceptual rainfall–runoff model (pd4-2par) to simulate the daily streamflows for six UK catchments for a set of climate scenarios using seven global circulation models (GCMs) and regional circulation models (RCMs). The performance of the combined methodology in reproducing observed streamflows is generally good. Results of future climate scenarios show significant variability between different catchments, and very large variability between different climate models. It is concluded that the GLM methodology is promising, and can readily be extended to support distributed hydrological modelling.
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Kritish De, S. Zeeshan Ali, Niladri Dasgpta, Virendra Prasad Uniyal, Jeyaraj Antony Johnson, and Syed Ainul Hussain. "Evaluating performance of four species distribution models using Blue-tailed Green Darner Anax guttatus (Insecta: Odonata) as model organism from the Gangetic riparian zone." Journal of Threatened Taxa 12, no. 14 (October 26, 2020): 16962–70. http://dx.doi.org/10.11609/jott.6106.12.14.16962-16970.
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In this paper we evaluated the performance of four species distribution models: generalized linear (GLM), maximum entropy (MAXENT), random forest (RF) and support vector machines (SVM) model, using the distribution of the dragonfly Blue-tailed Green Darner Anax guttatus in the Gangetic riparian zone between Bijnor and Kanpur barrage, Uttar Pradesh, India. We used forest cover type, land use, land cover and five bioclimatic variable layers: annual mean temperature, isothermality, temperature seasonality, mean temperature of driest quarter, and precipitation seasonality to build the models. We found that the GLM generated the highest values for AUC, Kappa statistic, TSS, specificity and sensitivity, and the lowest values for omission error and commission error, while the MAXENT model generated the lowest variance in variable importance. We suggest that researchers should not rely on any single algorithm, instead, they should test performance of all available models for their species and area of interest, and choose the best one to build a species distribution model.
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Truccolo, Wilson, Uri T. Eden, Matthew R. Fellows, John P. Donoghue, and Emery N. Brown. "A Point Process Framework for Relating Neural Spiking Activity to Spiking History, Neural Ensemble, and Extrinsic Covariate Effects." Journal of Neurophysiology 93, no. 2 (February 2005): 1074–89. http://dx.doi.org/10.1152/jn.00697.2004.
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Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.