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Journal articles on the topic 'Generalized linear model (GLM)'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Wilandari, Yuciana, Sri Haryatmi Kartiko, and Adhitya Ronnie Effendie. "ESTIMASI CADANGAN KLAIM MENGGUNAKAN GENERALIZED LINEAR MODEL (GLM) DAN COPULA." Jurnal Gaussian 9, no. 4 (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 cop

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MIFTAHUDDIN, ANANDA PRATAMA SITANGGANG, NORIZAN MOHAMED, and MAHARANI A. BAKAR. "MODELLING INDIAN OCEAN AIR TEMPERATURE USING ADDITIVE MODEL." Journal of Mathematical Sciences and Informatics 2, no. 1 (2022): 23–36. http://dx.doi.org/10.46754/jmsi.2022.06.003.

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In this study, we used the fluctuating air temperature dataset. The change is caused by data fluctuations, trend, seasonality, cyclicity and irregularities. The generalized additive model (GAM) data approach is used to describe these phenomena. The aim of this research is to find out the factors that affect the air temperature in the Indian Ocean, find a suitable model, and obtain the best model from three approximate methods, namely the Linear Model (LM), the Generalized Linear Model (GLM), and the GAM models, which use a dataset of factors that affect the temperature of the Indian Ocean (clo

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3

Garrido, José, and Jun Zhou. "Full Credibility with Generalized Linear and Mixed Models." ASTIN Bulletin 39, no. 1 (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 mechanis

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Bailey, Jason Robert, Davide Lauria, W. Brent Lindquist, Stefan Mittnik, and Svetlozar T. Rachev. "Hedonic Models of Real Estate Prices: GAM Models; Environmental and Sex-Offender-Proximity Factors." Journal of Risk and Financial Management 15, no. 12 (2022): 601. http://dx.doi.org/10.3390/jrfm15120601.

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We investigate the use of a P-spline generalized additive hedonic model (GAM) for real estate prices in large U.S. cities, contrasting their predictive efficiency against commonly used linear and polynomial-based generalized linear models (GLM). Using intrinsic and extrinsic factors available from Redfin, we show that the GAM model is capable of describing 84% to 92% of the variance in the expected ln(sales price), based upon 2021 data. In contrast, a strictly linear GLM accounted for 65% to 78% of the variance, while polynomial-based GLMs accounted for 82% to 88%. As climate change is becomin

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Salari Shahrbabaki, S., D. Dharmaprani, C. Strong, et al. "O066 Generalized linear model for characterisation of nocturnal arrhythmia." Sleep Advances 5, Supplement_1 (2024): A24. https://doi.org/10.1093/sleepadvances/zpae070.066.

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Abstract Background Generalised linear models (GLM) based on point processes have been previously shown helpful for characterising dynamics of sleep-disordered breathing (SDB) events using sleep stages, body position and history of SDB events, and characterising period limb movements. Episodes of non-sustained arrhythmias may occur during sleep in patients with underlying cardiac conditions, and the point process theory may help model their occurrence over time. Objective This study aims to develop a (GLM) to analyse the temporal patterns of nocturnal arrhythmia (NA) and their relationship wit

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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

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Labriji, Ali, Safae Msellek, and Abdelkrim Bennar. "The Sequential estimation of generalized linear model coefficients." Boletim da Sociedade Paranaense de Matemática 42 (May 6, 2024): 1–10. http://dx.doi.org/10.5269/bspm.63433.

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Generalized LinearModels (GLM) allows us to model the relationship between a response variable and one or more predictor variables while taking into account the distribution of the response variable. It is a useful tool for modeling data that do not follow a normal distribution and can be applied to a wide range of data types and problem settings. As data becomes increasingly relevant in our daily lives, the use of these models is becoming more important. However, this increase in importance also implies an increase in the complexity of estimation due to the volume of data that must be process

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Jun, Sunghae. "Text Data Analysis Using Generalized Linear Mixed Model and Bayesian Visualization." Axioms 11, no. 12 (2022): 674. http://dx.doi.org/10.3390/axioms11120674.

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Many parts of big data, such as web documents, online posts, papers, patents, and articles, are in text form. So, the analysis of text data in the big data domain is an important task. Many methods based on statistics or machine learning algorithms have been studied for text data analysis. Most of them were analytical methods based on the generalized linear model (GLM). For the GLM, text data analysis is performed based on the assumption of the error included in the given data and follows the Gaussian distribution. However, the GLM has shown limitations in the analysis of text data, including

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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 (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 y

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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 (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

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Cellamare, Matteo, Anna J. van Gestel, Hasan Alradhi, Frank Martin, and Arturo Moncada-Torres. "A Federated Generalized Linear Model for Privacy-Preserving Analysis." Algorithms 15, no. 7 (2022): 243. http://dx.doi.org/10.3390/a15070243.

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In the last few years, federated learning (FL) has emerged as a novel alternative for analyzing data spread across different parties without needing to centralize them. In order to increase the adoption of FL, there is a need to develop more algorithms that can be deployed under this novel privacy-preserving paradigm. In this paper, we present our federated generalized linear model (GLM) for horizontally partitioned data. It allows generating models of different families (linear, Poisson, logistic) without disclosing privacy-sensitive individual records. We describe its algorithm (which can be

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Yang, Zhaohui, Wei Zou, Haodong Liu, Ram P. Sharma, Mengtao Zhang, and Zhenhua Hu. "The Effect of Soil and Topography Factors on Larix gmelinii var. Principis-rupprechtii Forest Mortality and Capability of Decision Tree Binning Method and Generalized Linear Models in Predicting Tree Mortality." Forests 15, no. 12 (2024): 2060. http://dx.doi.org/10.3390/f15122060.

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Understanding the factors influencing individual tree mortality is essential for sustainable forest management, particularly for Prince Rupprech’s larch (Larix gmelinii var. Principis-rupprechtii) in North China’s natural forests. This study focused on 20 sample plots (20 × 20 m each) established in Shanxi Province, North China. This study compared three individual tree mortality models—Generalized Linear Model (GLM), Linear Discriminant Analysis (LDA), and Bayesian Generalized Linear Model (Bayesian GLM)—finding that both GLM and Bayesian GLM achieved approximately 0.87 validation accuracy on

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Alaqeeli, Omar, and Raad Alturki. "Evaluating the Performance of the Generalized Linear Model (glm) R Package Using Single-Cell RNA-Sequencing Data." Applied Sciences 13, no. 20 (2023): 11512. http://dx.doi.org/10.3390/app132011512.

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The glm R package is commonly used for generalized linear modeling. In this paper, we evaluate the ability of the glm package to predict binomial outcomes using logistic regression. We use single-cell RNA-sequencing datasets, after a series of normalization, to fit data into glm models repeatedly using 10-fold cross-validation over 100 iterations. Our evaluation criteria are glm’s Precision, Recall, F1-Score, Area Under the Curve (AUC), and Runtime. Scores for each evaluation category are collected, and their medians are calculated. Our findings show that glm has fluctuating Precision and F1-S

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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 (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 inp

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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 (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

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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 (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

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Clemente, Carina, Gracinda R. Guerreiro, and Jorge M. Bravo. "Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting." Risks 11, no. 9 (2023): 163. http://dx.doi.org/10.3390/risks11090163.

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Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. Standard Generalized Linear Models (GLM) frequency–severity models assume a linear relationship between a function of the response variable and the predictors, independence between the claim frequency and severity, and assign full credibility to the data. To overcome some of these restrictions, this paper investigates the predictive performance of Gradient Boosting with decision trees as base learners to model the c

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Somantri, Oman, and Linda Perdana Wanti. "A proposed model using Naïve Bayes and generalized linear models for early detection of heart attack risk." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1169–76. https://doi.org/10.11591/ijeecs.v33.i2.pp1169-1176.

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Timely identification of diseases, particularly heart attacks is crucial for individuals, particularly the elderly, to accurately anticipate the onset of the disease based on symtoms. The objective of this study is to develop a highly accurate model for early detection of heart disease using the Naïve Bayes (NB) and generalized linear model (GLM) techniques. In addition, another concern is the model’s subfar accuracy levels, promting the implementation of measures to optimize it. The suggested approach fot optimization involves the utilization of a genetic algorithm (GA). The resear

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Bar-Lev, Shaul K., Xu Liu, Ad Ridder, and Ziyu Xiang. "Generalized Linear Model (GLM) Applications for the Exponential Dispersion Model Generated by the Landau Distribution." Mathematics 12, no. 13 (2024): 2021. http://dx.doi.org/10.3390/math12132021.

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The exponential dispersion model (EDM) generated by the Landau distribution, denoted by EDM-EVF (exponential variance function), belongs to the Tweedie scale with power infinity. Its density function does not have an explicit form and, as of yet, has not been used for statistical aspects. Out of all EDMs belonging to the Tweedie scale, only two EDMs are steep and supported on the whole real line: the normal EDM with constant variance function and the EDM-EVF. All other absolutely continuous steep EDMs in the Tweedie scale are supported on the positive real line. This paper aims to accomplish a

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Somantri, Oman, and Linda Perdana Wanti. "A proposed model using Naïve Bayes and generalized linear models for early detection of heart attack risk." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1169. http://dx.doi.org/10.11591/ijeecs.v33.i2.pp1169-1176.

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<span>Timely identification of diseases, particularly heart attacks is crucial for individuals, particularly the elderly, to accurately anticipate the onset of the disease based on symtoms. The objective of this study is to develop a highly accurate model for early detection of heart disease using the Naïve Bayes (<span>NB) and generalized linear model (GLM) techniques. In addition, another concern is the model’s subfar accuracy levels, promting the implementation of measures to optimize it. The suggested approach fot optimization involves</span> <span>the utilization o

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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 (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 distribute

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Oktaviani, Devani, Nabila Zahra, and Nurfadhlina Abdul Halim. "Determination of Pure Health Insurance Premiums Using Generalized Linear Models (GLM) with Poisson Distribution." International Journal of Global Operations Research 5, no. 2 (2024): 88–92. http://dx.doi.org/10.47194/ijgor.v5i2.308.

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Insurance companies need a method to help companies determine premium prices that are appropriate to the risks they face. In other words, it is also necessary to know the variables that influence premium prices using Generalized Linear Models (GLM) by generalizing the linear regression model to model the relationship between the dependent variable and the independent variable. The aim of this research is to determine the variables that influence premium prices and determine the pure health insurance premium using the GLM method.

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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 (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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Saidi, Subian, Netti Herawati, and Khoirin Nisa. "Modeling with generalized linear model on covid-19: Cases in Indonesia." International Journal of Electronics and Communications Systems 1, no. 1 (2021): 25–32. http://dx.doi.org/10.24042/ijecs.v1i1.9299.

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The ongoing Covid-19 outbreak has made scientists continue to research this Covid-19 case. Most of the research carried out is on the prediction and modeling of Covid-19 data. This study will also discuss Covid-19 data modeling. The model that is widely used is the linear model. However, if the classical assumption of normality is not met, a special method is needed. The method that can overcome this is the generalized linear model (GLM), with the assumption that the data is distributed in an exponential family. The distribution used in this study is the Gaussian, Poisson, and Gamma distributi

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Bektas, Volkan, Pete Bettinger, Nate Nibbelink, et al. "Habitat Suitability Modeling of Rare Turkeybeard (Xerophyllum asphodeloides) Species in the Talladega National Forest, Alabama, USA." Forests 13, no. 4 (2022): 490. http://dx.doi.org/10.3390/f13040490.

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This study focused on the rare and threatened plant species eastern turkeybeard (Xerophyllum asphodeloides (L.) Nutt.) and its presence or absence in the Talladega National Forest in Alabama, USA. An ensemble suitable habitat map was developed using four different modeling methods (MaxEnt, Generalized Linear Model, Generalized Additive Model, and Random Forest). AUC evaluation scores for each model were 0.99, 0.96, 0.98, and 0.99, respectively. Biserial correlation scores for models ranged from 0.71 (GLM) to 0.94 (RF). The four different models agreed suitable habitat was found to cover 159.57

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Romadhona, Wulanova, and Venita Syavera. "Generalized Linear Model Menggunakan Distrbusi Lognormal dan Gamma: Aplikasi Terhadap Indeks Demokrasi Indonesia di Jawa Barat." AKSIOMA : Jurnal Sains Ekonomi dan Edukasi 2, no. 1 (2025): 117–26. https://doi.org/10.62335/gbezv413.

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Generalized Linear Models (GLM) is a statistical approach that generalize linear regression models to handle response variables that are not necessarily normal. This research uses Gamma and Lognormal distributions to analyze the effect of Labor Force Participation Rate, Gini Ratio, and Percentage of Working Population on the Indonesia Democracy Index in West Java during 2021-2023. Both distributions are used and compared using AIC. Some of the results of this research are: 1) Lognormal GLM has a lower AIC, so it is better to use than Gamma GLM. 2) From these two models, obtained the same analy

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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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Yulita, Tiara, and Adhitya Ronnie Effendie. "ESTIMATION OF IBNR AND RBNS RESERVES USING RDC METHOD AND GAMMA GENERALIZED LINEAR MODEL." MEDIA STATISTIKA 15, no. 1 (2022): 24–35. http://dx.doi.org/10.14710/medstat.15.1.24-35.

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Estimation of claims reserves is a very important role for insurance companies because the information will be used to assess the insurance company’s ability to meet future claim payment obligations. In practice, claims reserves are divided into two Incurred but Not Reported (IBNR) and Reported but Not Settled (RBNS). Reserving by Detailed Conditioning (RDC) is one of the individual methods that can estimate claims reserves of both the IBNR and RBNS, which involves detailed condition so-called claim characteristics, and some information else so-called background variable. The result of estimat

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XU, SHIZHONG. "Testing Hardy–Weinberg disequilibrium using the generalized linear model." Genetics Research 94, no. 6 (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 pos

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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. Study

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Xie, Shengkun, and Rebecca Luo. "Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions." Mathematics 10, no. 10 (2022): 1630. http://dx.doi.org/10.3390/math10101630.

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Predictive modeling is a critical technique in many real-world applications, including auto insurance rate-making and the decision making of rate filings review for regulation purposes. It is also important in predicting financial and economic risk in business and economics. Unlike testing hypotheses in statistical inference, results obtained from predictive modeling serve as statistical evidence for the decision making of the underlying problem and discovering the functional relationship between the response variable and the predictors. As a result of this, the variable importance measures be

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Owiredu, Solomon Amoah, Shem Otoi Onyango, Eun-A. Song, Kwang-Il Kim, Byung-Yeob Kim, and Kyoung-Hoon Lee. "Enhancing Chub Mackerel Catch Per Unit Effort (CPUE) Standardization through High-Resolution Analysis of Korean Large Purse Seine Catch and Effort Using AIS Data." Sustainability 16, no. 3 (2024): 1307. http://dx.doi.org/10.3390/su16031307.

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Accurate determination of fishing effort from Automatic Identification System (AIS) data improves catch per unit effort (CPUE) estimation and precise spatial management. By combining AIS data with catch information, a weighted distribution method is applied to allocate catches across various fishing trajectories, accounting for temporal dynamics. A Generalized Linear Model (GLM) and Generalized Additive Model (GAM) were used to examine the influence of spatial–temporal and environmental variables (year, month, Sea Surface Temperature (SST), Sea Surface Salinity (SSS), current velocity, depth,

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Mukhoty, Bhaskar, Debojyoti Dey, and Purushottam Kar. "Corruption-Tolerant Algorithms for Generalized Linear Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9243–50. http://dx.doi.org/10.1609/aaai.v37i8.26108.

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This paper presents SVAM (Sequential Variance-Altered MLE), a unified framework for learning generalized linear models under adversarial label corruption in training data. SVAM extends to tasks such as least squares regression, logistic regression, and gamma regression, whereas many existing works on learning with label corruptions focus only on least squares regression. SVAM is based on a novel variance reduction technique that may be of independent interest and works by iteratively solving weighted MLEs over variance-altered versions of the GLM objective. SVAM offers provable model recovery

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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 (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 FG

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Chatrabgoun, Omid, Alireza Daneshkhah, Parisa Torkaman, Mark Johnston, Nader Sohrabi Safa, and Ali Kashif Bashir. "Covariate-adjusted construction of gene regulatory networks using a combination of generalized linear model and penalized maximum likelihood." PLOS ONE 20, no. 1 (2025): e0309556. https://doi.org/10.1371/journal.pone.0309556.

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Many machine learning techniques have been used to construct gene regulatory networks (GRNs) through precision matrix that considers conditional independence among genes, and finally produces sparse version of GRNs. This construction can be improved using the auxiliary information like gene expression profile of the related species or gene markers. To reach out this goal, we apply a generalized linear model (GLM) in first step and later a penalized maximum likelihood to construct the gene regulatory network using Glasso technique for the residuals of a multi-level multivariate GLM among the ge

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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 (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 init

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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 (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 tempora

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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 (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, Ma

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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 (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 Ont

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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 (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

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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 (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 bertu

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Díaz Martínez, Zuleyka, José Fernández Menéndez, and Luis Javier García Villalba. "Tariff Analysis in Automobile Insurance: Is It Time to Switch from Generalized Linear Models to Generalized Additive Models?" Mathematics 11, no. 18 (2023): 3906. http://dx.doi.org/10.3390/math11183906.

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Generalized Linear Models (GLMs) are the standard tool used for pricing in the field of automobile insurance. Generalized Additive Models (GAMs) are more complex and computationally intensive but allow taking into account nonlinear effects without the need to discretize the explanatory variables. In addition, they fit perfectly into the mental framework shared by actuaries and are easier to use and interpret than machine learning models, such as trees or neural networks. This work compares both the GLM and GAM approaches, using a wide sample of policies to assess their differences in terms of

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Yermolaev, Oleg, and Svetlana Mukharamova. "Statistical Analysis and Modeling of Suspended Sediment Yield Dependence on Environmental Conditions." Water 15, no. 14 (2023): 2639. http://dx.doi.org/10.3390/w15142639.

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This paper describes the modelling of suspended sediment yield in a plains region in the European part of Russia (EPR) and its prediction for ungauged catchments. The studied plains area, excluding the Caucasus and Ural Mountains, covers 3.5 × 106 km2 of the total area of about 3.8 × 106 km2. Multiple regression methods, such as a generalized linear model (GLM) and a generalized additive model (GAM), are used to construct the models. The research methodology is based on a catchment approach. There are 49,516 river basins with an average area of about 75 km2 in the plain regions. The suspended

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Sihombing, Pardomuan Robinson. "COMPARISON OF GLM, GLMM AND GEE POISSON MATHEMATICAL MODELING PERFORMANCE (Case Study: Number of Pulmonary Tuberculosis Patients in Indonesia in 2019-2021)." Jurnal TAMBORA 6, no. 3 (2022): 102–6. http://dx.doi.org/10.36761/jt.v6i3.2081.

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This study aims to compare the performance of data modeling with Poisson regression with Generalized Linear Model (GLM), Generalized Linear Mixed Model (GLMM), and Generalized Estimating Equation (GEE) modeling. The case study used is a factor that affects the number of Pulmonary Tuberculosis cases in Indonesia with panel data. Based on the AIC criteria, the smallest BIC and RMSE GLMM models perform better than GLM and GEE. In addition, GLMM modeling also has a coefficient of determination value. The results showed that the percentage of the population smoking and the percentage of Unmet Keseh

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Slime, Mekdad, Abdellah Ould Khal, Abdelhak Zoglat, Mohammed El Kamli, and Brahim Batti. "Optimizing Automobile Insurance Pricing: A Generalized Linear Model Approach to Claim Frequency and Severity." Statistics, Optimization & Information Computing 13, no. 6 (2025): 2294–315. https://doi.org/10.19139/soic-2310-5070-2157.

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Morocco's insurance sector, particularly auto insurance, is experiencing significant growth despite economic challenges. To remain competitive, companies must innovate and adjust their pricing to meet customer expectations and strengthen their market position. Traditionally, actuaries have used the linear model to assess the impact of explanatory variables on the frequency and severity of claims. However, this model has limitations that do not always accurately reflect the reality of claims or costs, especially in auto insurance. Our study adopted the generalized linear model (GLM) to address

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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 (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 GL

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Pang, Johnny, and Ryan Sonn. "The Intricacies of College and University Closures: A Generalized Linear Model Perspective." International Journal of Business and Management 20, no. 4 (2025): 86. https://doi.org/10.5539/ijbm.v20n4p86.

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This study delves into the intricate and multifaceted factors driving state-level college and university closures, leveraging publicly accessible variables through a Generalized Linear Model (GLM) analysis, in contrast to Multiple Discriminant Analysis (MDA). The analysis identifies key determinants of closures, including institutional endowment, tuition, and the percentage of in-state students. A pivotal contribution of this research is the development of a predictive Z-score model and ranges, offering robust and efficient tools for classifying higher education institution closures with enhan

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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 (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 wide

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Rosenlund, Stig. "Dispersion Estimates for Poisson and Tweedie Models." ASTIN Bulletin 40, no. 1 (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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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 (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 observati

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