Relevant bibliographies by topics / Generalized linear model (GLM)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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  6. Reports

Academic literature on the topic 'Generalized linear model (GLM)'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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

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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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Dissertations / Theses on the topic "Generalized linear model (GLM)"

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Park, Jeanseong. "Longitudinal Data Analysis Using Generalized Linear Model with Missing Responses." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33355.

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Longitudinal studies rely on data collected at several occasions from a set of selected individuals. The purpose of these studies is to use a regression-type model to express a response variable as a function of explanatory variables, or covariates. In this thesis, we use marginal models for the analysis of such data, which, coupled with the method of estimating equations, provide estimators of the main regression parameter. When some of the responses are missing or there is error in the recorded covariates, the original estimating equation may be biased. We use techniques available in the literature to modify it and regain the unbiasedness property. We prove the asymptotic normality of the regression estimator obtained under these more realistic circumstances, and provide theoretical and numerical examples to illustrate this approach.
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Hatzinger, Reinhold. "A GLM framework for item response theory models. Reissue of 1994 Habilitation thesis." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/1384/1/document.pdf.

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The aim of the monograph is to contribute towards bridging the gap between methodological developments that have evolved in the social sciences, in particular in psychometric research, and methods of statistical modelling in a more general framework. The first part surveys certain special psychometric models (often referred to as Rasch family of models) that share common properties: separation of parameters describing qualities of the subject under investigation and parameters related to properties of the situation under which the response of a subject is observed. Using conditional maximum likelihood estimation, both types of parameters may be estimated independently from each other. In particular, the Rasch model, the rating scale model, the partial credit model, hybrid types, and linear extensions thereof are treated. The second part reviews basic ideas of generalized linear models (GLMs) as an an excellent framework for unifying different approaches and providing a natural, technical background for model formulation, estimation and testing. This is followed by a short introduction to the software package GLIM chosen to illustrate the formulation of psychometric models in the GLM framework. The third part is the main part of this monograph and shows the application of generalized linear models to psychometric approaches. It gives a unified treatment of Rasch family models in the context of log-linear models and contains some new material on log-linear longitudinal modelling. The last part of the monograph is devoted to show the usefulness of the latent variable approach in a variety of applications, such as panel, cross-over, and therapy evaluation studies, where standard statistical analysis does not necessarily lead to satisfactory results. (author´s abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Bracken, Jason. "A Consensus Model for Predicting the Distribution of the Threatened Plant Telephus Spurge (Euphorbia Telephioides)." Miami University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=miami1480622681613979.

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Leodolter, Johannes. "A Statistical Analysis of the Lake Levels at Lake Neusiedl." Austrian Statistical Society, 2008. http://epub.wu.ac.at/5634/1/296%2D1009%2D1%2DSM.pdf.

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A long record of daily data is used to study the lake levels of Lake Neusiedl, a large steppe lake at the eastern border of Austria. Daily lake level changes are modeled as functions of precipitation, temperature, and wind conditions. The occurrence and the amount of daily precipitation are modeled with logistic regressions and generalized linear models.
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Drakenward, Ellinor, and Emelie Zhao. "Modeling risk and price of all risk insurances with General Linear Models." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275696.

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Denna kandidatexamen ligger inom området matematisk statistik. I samarbete med försäkringsbolaget Hedvig syftar denna avhandling till att utforska en ny metod för hantering av Hedvigs försäkringsdata genom att bygga en prissättningsmodell för alla riskförsäkringar med generaliserade linjära modeller. Två generaliserade linjära modeller byggdes, där den första förutspår frekvensen för ett anspråk och den andra förutspår svårighetsgraden. De ursprungliga uppgifterna delades in i 9 förklarande variabler. Båda modellerna inkluderade fem förklarande variabler i början och reducerades sedan. Minskningen resulterade i att fyra av fem egenskaper var förklarande signifikanta i frekvensmodellen och endast en av de fem var förklarande signifikanta i svårighetsmodellen. Var och en av modellerna erhöll relativa risker för nivåerna av deras förklarande variabler. De relativa riskerna resulterade i en total risk för varje nivå. Genom multiplicering av en skapad basnivå med en uppsättning kombination av riskparametrar kan premien för en vald kund erhållas.
Det här kandidatexamensarbetet ligger inom ämnet matematisk statistik. Jag samarbete med försäkringsbolaget Hedvig, avser uppsatsen att undersöka en ny metod att hantera Hedvigs försäkringsdata genom att bygga en prissättningsmodell för drulleförsäkring med hjälp av generaliserade linjära modeller. Två modeller skapades varav den första förutsättningen frekvensen av ett försäkringsanspråk och den andra förutsäger storleken. Originaldatan var indelad i 9 förklarande variabler. Båda modellerna innehöll till en början fem förklarande variabler, vilka sedan reducerades till fyra respektive en variabler i de motsvarande modellerna. Från varje modell kunde sedan de relativa riskerna tas fram för varje kategori av de förklarande variablerna. Tillsammans bildades sedan totalrisken för alla grupper.
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Vermelho, Alexandre Filipe Correia Cajana. "Calculating best estimates in a GLM framework. Frequency/severity models vs total loss models." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/7040.

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Mestrado em Ciências Actuariais
When using generalized linear models to predict future claim payments, should actuaries use separate frequency/severity models or a single loss cost model? This is the question this paper addresses, covering some theoretical background, testing both alternatives on real data from the Industrial Multiple Risks (IMR) sub-­‐branch and analysing its results. Data was provided by 7 companies operating in Portugal in the years 2010 and 2011, who own a 70% share of the Portuguese IMR market and was collected by Associação Portuguesa de Seguradores (APS).
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Dittrich, Regina, Brian Francis, Reinhold Hatzinger, and Walter Katzenbeisser. "A Paired Comparison Approach for the Analysis of Sets of Likert Scale Responses." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/654/1/document.pdf.

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This paper provides an alternative methodology for the analysis of a set of Likert responses measured on a common attitudinal scale when the primary focus of interest is on the relative importance of items in the set. The method makes fewer assumptions about the distribution of the responses than the more usual approaches such as comparisons of means, MANOVA or ordinal data methods. The approach transforms the Likert responses into paired comparison responses between the items. The complete multivariate pattern of responses thus produced can be analysed by an appropriately reformulated paired comparison model. The dependency structure between item responses can also be modelled flexibly. The advantage of this approach is that sets of Likert responses can be analysed simultaneously within the Generalized Linear Model framework, providing standard likelihood based inference for model selection. This method is applied to a recent international survey on the importance of environmental problems. (author's abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Lewi, Jeremy. "Sequential optimal design of neurophysiology experiments." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28201.

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Thesis (M. S.)--Biomedical Engineering, Georgia Institute of Technology, 2009.
Committee Co-Chair: Butera, Robert; Committee Co-Chair: Paninski, Liam; Committee Member: Isbell, Charles; Committee Member: Rozell, Chris; Committee Member: Stanley, Garrett; Committee Member: Vidakovic, Brani.
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Carlsson, Rebecka. "Skogsväxters utbredning i relation till pH, latitud och trädsammansättning : Exkursion för ekologiundervisning." Thesis, Linköpings universitet, Biologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-131633.

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This study investigated the impact of three edaphic factors on the distribution of forest plants in Sweden. Based on 2657 plots with 22 common species, Canonical Correspondence Analysis (CCA) and Generalized-linear-model (GLM) were performed with pH measurements in the top layer of the soil, latitude and deciduous tree proportion as explanatory variables. Variation of the species occurrence could to a substantial degree be explained by pH, latitude and proportion of timber volume of deciduous tree species. Furthermore, the majority of species were affected by the studied environmental variables. Therefore, these factors have an important role in the ecological interactions in the forest. All species also showed broad pH-niches with many occurrences spread out within the species entire pH-range. Finally, the study relates to educational science through designing a meaningful excursion for secondary school when teaching ecology.
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Vasconcelos, Priscila Elida de Medeiros. "Estudo da relação entrevariáveis térmicas e o desempenho de estudantes da Academia de Polícia Militar do Estado da Paraíba." Universidade Federal da Paraí­ba, 2013. http://tede.biblioteca.ufpb.br:8080/handle/tede/5240.

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Made available in DSpace on 2015-05-08T14:53:30Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2932778 bytes, checksum: ccea3581e43612c0defaaf48b149ea0e (MD5) Previous issue date: 2013-04-23
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Air temperature is regarded as an environmental factor with greatest influence on worker performance. However, little information exists about its influence on academic environment. Therefore, this study aims to analyze the effects caused by thermal discomfort on students performance. Thus, students of Military Police Academic Center of Paraíba were subjected to a series of psychometric tests that assessed their reasoning skills in five specific areas: verbal, abstract, mechanical, spatial and numerical reasoning; on three sessions under different temperatures: 20 ° C, 24 ° C and 30 ° C. The optimal temperatures found to thermal sensations and thermal comfort neutrality were approximately 23 ° C and 21 ° C, respectively, which reflects a relatively low thermal preference, compared to that provided by ISO 7730/2005 standard. The performance of students varied according to the nature of the tests. They demonstrated motivation to perform the tests, regardless of which thermal conditions that were subjected. However, with the aid of generalized linear models (GLM), it was found that the dry bulb temperature, globe temperature, and relative humidity influence the overall performance of the students (pseudo-R ² = 0.8497).
A temperatura do ar é considerada um dos fatores ambientais com maior influência sobre o desempenho do trabalhador. Entretanto, existe pouca informação sobre a influência da mesma em ambiente escolar. Com isso, este estudo pretende analisar os efeitos causados pelo desconforto térmico sobre o desempenho de estudantes. Para tanto, cadetes da Polícia Militar da Paraíba foram submetidos a uma série de testes psicométricos que avaliaram seu raciocínio em cinco áreas específicas: raciocínio verbal, abstrato, mecânico, espacial e numérico. Os testes foram aplicados durante três sessões sob diferentes temperaturas: 20°C, 24°C e 30°C. As temperaturas ideais para as sensações de neutralidade térmica e conforto térmicoencontrados foram de aproximadamente 23°C e 21°C, respectivamente, que reflete numa preferência térmica relativamente baixa, se comparado ao previsto pela norma ISO 7730/2005. O desempenho dos estudantes em relação à temperatura variou de acordo com a natureza dos testes. Os mesmos se demonstram motivados para realizar os testes, independente das condições térmicas às quais estavam submetidos. Entretanto, com o auxílio de Modelos lineares generalizados (MLG), foi possívelconstatar que as temperaturas de bulbo seco, temperatura de globo e umidade relativa do ar exercem influênciano desempenho geral dos cadetes (pseudo-R² = 0,8497).
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Books on the topic "Generalized linear model (GLM)"

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Robert, Gilchrist, Francis B, and Whittaker J, eds. Generalized linear models: Proceedings of the GLIM 85 Conference, held in Lancaster, UK, Sept. 16-19, 1985. Berlin: Springer-Verlag, 1985.

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Extending the linear model with R: Generalized linear, mixed effects and nonparametric regression models. Boca Raton: Taylor & Francis, 2016.

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Miles, Jeremy. General and generalised linear models. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0017.

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This chapter discusses general and generalised linear models (GLM and GLZ respectively). It outlines GLMs (mean, properties of GLMs and the mean), samples and populations, comparison of two groups of data, multiple regression and the GLM, analysis of variance (ANOVA) and the GLM, GLM in SPSS, and the GLZ).
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(Editor), Brian Francis, Mick Green (Editor), and Clive Payne (Editor), eds. The GLIM System: Release 4 Manual (Generalized Linear Interactive Modelling). Oxford University Press, USA, 1993.

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Portides, George. Robust regression with application to generalized linear model. 1997.

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Francis, B., and R. Gilchrist. Generalized Linear Models: Proceedings of the Glim 85 Conference, Held in Lancaster, Uk, Sept 16-19, 1985 (Lecture Notes in Statistics). Springer, 1986.

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Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition. Taylor & Francis Group, 2016.

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Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Texts in Statistical Science). Chapman & Hall/CRC, 2005.

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Walsh, Bruce, and Michael Lynch. Short-term Changes in the Mean: 2. Truncation and Threshold Selection. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0014.

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The selection intensity, the mean change in a trait within a generation expressed in phenotypic standard deviations, provides an important metric for comparing the strength of selection over designs. Further, under truncation selection (only individuals above some threshold leave offspring), the selection intensity is a function of the fraction saved, and hence the breeder's equation is often expressed in terms of the selection intensity. An important special case of truncation selection is a threshold trait, wherein an individual only expresses a particular phenotype when its underlying liability value exceeds some threshold. This chapter examines selection on such traits, and generalizes this binary-trait setting (with binomial residuals) to other classes of discrete traits, wherein some underling linear model (generating the threshold) is this transformed via a generalized linear mixed model into an observed trait value.
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Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.
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Book chapters on the topic "Generalized linear model (GLM)"

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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "More generalized linear modelling." In Practical R for biologists: an introduction, 171–86. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0171.

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Abstract This chapter employs generalized linear modelling using the function glm when we know that variances are not constant with one or more explanatory variables and/or we know that the errors cannot be normally distributed, for example, they may be binary data, or count data where negative values are impossible, or proportions which are constrained between 0 and 1. A glm seeks to determine how much of the variation in the response variable can be explained by each explanatory variable, and whether such relationships are statistically significant. The data for generalized linear models take the form of a continuous response variable and a combination of continuous and discrete explanatory variables.
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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "More generalized linear modelling." In Practical R for biologists: an introduction, 171–86. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0015.

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Abstract This chapter employs generalized linear modelling using the function glm when we know that variances are not constant with one or more explanatory variables and/or we know that the errors cannot be normally distributed, for example, they may be binary data, or count data where negative values are impossible, or proportions which are constrained between 0 and 1. A glm seeks to determine how much of the variation in the response variable can be explained by each explanatory variable, and whether such relationships are statistically significant. The data for generalized linear models take the form of a continuous response variable and a combination of continuous and discrete explanatory variables.
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Payne, Clive, and Janet Webb. "GLIM 3.77." In Generalized Linear Models, 137–46. New York, NY: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4615-7070-7_15.

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Nelder, J. A. "Quasi-Likelihood and GLIM." In Generalized Linear Models, 120–27. New York, NY: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4615-7070-7_13.

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Palmgren, Juni, and Anders Ekholm. "GLIM for Latent Class Analysis." In Generalized Linear Models, 128–36. New York, NY: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4615-7070-7_14.

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Gilchrist, Robert. "Introduction: GLIM and Generalized Linear Models." In Generalized Linear Models, 1–5. New York, NY: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4615-7070-7_1.

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Diggle, Peter J. "Comparing Estimated Spectral Densities Using GLIM." In Generalized Linear Models, 35–43. New York, NY: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4615-7070-7_5.

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Denuit, Michel, Donatien Hainaut, and Julien Trufin. "Generalized Linear Models (GLMs)." In Springer Actuarial, 97–196. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25820-7_4.

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Eilers, Paul H. C., and Brian D. Marx. "Generalized Linear Models with P-splines." In Advances in GLIM and Statistical Modelling, 72–77. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_12.

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Härdle, Wolfgang K., and Berwin A. Turlach. "Nonparametric Approaches to Generalized Linear Models." In Advances in GLIM and Statistical Modelling, 213–25. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_33.

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Conference papers on the topic "Generalized linear model (GLM)"

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Naufal, N., S. Devila, and D. Lestari. "Generalized linear model (GLM) to determine life insurance premiums." In PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132463.

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Yu, Lele, Lingyu Wang, Yingxia Shao, Long Guo, and Bin Cui. "GLM+: An Efficient System for Generalized Linear Models." In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2018. http://dx.doi.org/10.1109/bigcomp.2018.00050.

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Jamilatuzzahro, Rezzy Eko Caraka, Dedi Aprinaldy, and Asma Mahadi. "Generalized linear model multivariate poisson with artificial marginal (GLM-MPAM): Application of vehicle insurance." In THE 2018 UKM FST POSTGRADUATE COLLOQUIUM: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2018 Postgraduate Colloquium. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5111220.

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Lu, Shiyin, Guanghui Wang, Yao Hu, and Lijun Zhang. "Multi-Objective Generalized Linear Bandits." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/427.

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In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objectives. MOB has found many real-world applications as varied as online recommendation and network routing. On the other hand, these applications typically contain contextual information that can guide the learning process which, however, is ignored by most of existing work. To utilize this information, we associate each arm with a context vector and assume the reward follows the generalized linear model (GLM). We adopt the notion of Pareto regret to evaluate the learner's performance and develop a novel algorithm for minimizing it. The essential idea is to apply a variant of the online Newton step to estimate model parameters, based on which we utilize the upper confidence bound (UCB) policy to construct an approximation of the Pareto front, and then uniformly at random choose one arm from the approximate Pareto front. Theoretical analysis shows that the proposed algorithm achieves an \tilde O(d\sqrt{T}) Pareto regret, where T is the time horizon and d is the dimension of contexts, which matches the optimal result for single objective contextual bandits problem. Numerical experiments demonstrate the effectiveness of our method.
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Muda, Nora. "The performance of M-based generalized linear model (GLM) procedures based on the coverage probability." In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, 2009. http://dx.doi.org/10.1109/nabic.2009.5393419.

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Jrad, Hanen, Jean Luc Dion, Franck Renaud, Imad Tawfiq, and Mohamed Haddar. "Non-Linear Generalized Maxwell Model for Dynamic Characterization of Viscoelastic Components and Parametric Identification Techniques." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70264.

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Viscoelastic components are incorporated into automobile and aerospace structures system in order to damp mechanical vibrations. Viscoelastic components are a key element in designing desired dynamic behaviour of mechanical systems. Viscoelastic components dynamic characteristics are often very complex, due to the dependence of its response on several variables, such as frequency, amplitude, preload, and temperature. These dependencies can be critical in capturing the mechanical proprieties and so non linear dynamical behaviour may appear. Assuming that non linearities are due to non linear elasticity, the non linear Generalized Maxwell Model (GMM) is proposed to characterize dynamics of viscoelastic components. Parameters of GMM are identified from Dynamic Mechanical Analysis (DMA) tests for different excitation frequencies. A particular result from identification is that the non linear stiffness is dependent upon displacement amplitude and static displacement under static preload. The significance of this result is that the non linear dynamics of the viscoelastic component can be represented by a simple analytical model capable to produce accurate results. Comparison between measurements and simulations of dynamic stiffness of viscoelastic component has been carried on.
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Edwards, C. L., and M. L. Edwards. "A generalized electrostatic micro-mirror (GEM) model for a two-axis convex piecewise linear shaped MEMS mirror." In SPIE Defense, Security, and Sensing, edited by Thomas George, M. Saif Islam, and Achyut K. Dutta. SPIE, 2009. http://dx.doi.org/10.1117/12.818838.

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Hanisah, Suhaimi, and Jamaludin Suhaila. "Generalized linear models (GLMs) approach in modeling rainfall data over Johor area." In PROCEEDINGS OF THE 21ST NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM21): Germination of Mathematical Sciences Education and Research towards Global Sustainability. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4887723.

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Scarponcini, Paul. "Generalized model for linear referencing." In the seventh ACM international symposium. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/320134.320149.

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Ahn, Dohyun, and Dongwook Shin. "Ordinal Optimization with Generalized Linear Model." In 2020 Winter Simulation Conference (WSC). IEEE, 2020. http://dx.doi.org/10.1109/wsc48552.2020.9384070.

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Reports on the topic "Generalized linear model (GLM)"

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Fienberg, Stephen E., and Andrew C. Thomas. Exploring the Consequences of IED Deployment with a Generalized Linear Model Implementation of the Canadian Traveller Problem. Fort Belvoir, VA: Defense Technical Information Center, November 2010. http://dx.doi.org/10.21236/ada547012.

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