Relevant bibliographies by topics / Generalized linear model (GLM)

Contents

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

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

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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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 (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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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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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 lit
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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 li
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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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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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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. Minskni
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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<br>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
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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 variable
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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.<br>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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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<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>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 Pa
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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. Springer-Verlag, 1985.

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Novikov, Anatoliy, Tat'yana Solodkaya, Aleksandr Lazerson, and Viktor Polyak. Econometric modeling in the GRETL package. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1732940.

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The tutorial describes the capabilities of the GRETL statistical package for computer data analysis and econometric modeling based on spatial data and time series. Using concrete economic examples, GRETL considers classical and generalized models of linear and nonlinear regression, methods for detecting and eliminating multicollinearity, models with variable structure, autoregressive processes, methods for testing and eliminating autocorrelation, as well as discrete choice models and systems of simultaneous equations.&#x0D; For the convenience of users, the tutorial contains all the task data
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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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Gilchrist, R. GLIM 82 : Proceedings of the International Conference on Generalised Linear Models: Proceedings of the International Conference on Generalised Linear Models. Springer London, Limited, 2012.

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

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Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Group, 2013.

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Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Group, 2004.

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Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Group, 2005.

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Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Group, 2005.

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Book chapters on the topic "Generalized linear model (GLM)"

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Wüthrich, Mario V., and Michael Merz. "Generalized Linear Models." In Springer Actuarial. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_5.

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AbstractThis chapter discusses state-of-the-art statistical modeling in insurance and actuarial science, which is the generalized linear model (GLM). We discuss GLMs in the light of claim count and claim size modeling, we present feature engineering, model fitting, model selection, over-dispersion, zero-inflated claim counts problems, double GLMs, and insurance-specific issues such as the balance property for having unbiasedness.
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Salinas Ruíz, Josafhat, Osval Antonio Montesinos López, Gabriela Hernández Ramírez, and Jose Crossa Hiriart. "Generalized Linear Models." In Generalized Linear Mixed Models with Applications in Agriculture and Biology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32800-8_2.

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AbstractIn the generalized linear model (GLM) (which is not highly general) y = Xβ + ϵ, the response variables are normally distributed, with constant variance across the values of all the predictor variables, and are linear functions of the predictor variables. Transformations of data are used to try to force the data into a normal linear regression model or to find a non-normal-type response variable transformation (discrete, categorical, positive continuous scale, etc.) that is linearly related to the predictor variables; however, this is no longer necessary. Instead of using a normal distr
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Islam, M. Ataharul, and Soma Chowdhury Biswas. "Simple GLM." In Generalized Linear Models and Extensions. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4726-2_3.

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Islam, M. Ataharul, and Soma Chowdhury Biswas. "Bayesian Approach for GLM." In Generalized Linear Models and Extensions. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4726-2_11.

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Islam, M. Ataharul, and Soma Chowdhury Biswas. "GLM for Big Data Analytics." In Generalized Linear Models and Extensions. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4726-2_12.

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Islam, M. Ataharul, and Soma Chowdhury Biswas. "Estimation and Tests for Univariate GLM." In Generalized Linear Models and Extensions. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4726-2_4.

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Islam, M. Ataharul, and Soma Chowdhury Biswas. "Extension of GLM for Bivariate Data." In Generalized Linear Models and Extensions. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4726-2_8.

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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "More generalized linear modelling." In Practical R for biologists: an introduction. 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 tak
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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "More generalized linear modelling." In Practical R for biologists: an introduction. 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 tak
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Islam, M. Ataharul, and Soma Chowdhury Biswas. "Multivariate Data and GLM: Generalized Estimating Equations." In Generalized Linear Models and Extensions. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4726-2_6.

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

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Panwal, Sammy Joel, Nathaniel Wambebe, and Adindu C. Kingsley. "Assessing the Impact of Generator-Related Air Pollution on Stress Levels among Printing Press Workers in Abuja, Nigeria: A Generalized Linear Model (GzLM) Analysis." In Africa International Conference on Clean Energy and Energy Storage. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-ij22km.

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The impact of air pollution on public and environmental remains huge burden in Nigeria that has not been properly addressed. Poverty energy supply has further exacerbated the current situations making business to rely on generators for energy power supply. This study aims to assess the impact of generators-induced air pollution on stress levels among printing press workers in a specific setting Abuja. This cross-sectional designed was adopted to conduct this study among printing press workers who use generators in Abuja. A total of 508 workers were selected using simple random sampling. Data o
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Cheng, Yuxuan. "Sampling Point Method: A Generalized Algorithm for Class-Incremental Learning of Linear Model." In 2024 International Symposium on Intelligent Robotics and Systems (ISoIRS). IEEE, 2024. http://dx.doi.org/10.1109/isoirs63136.2024.00050.

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Mu, Zhanhong. "Research on Traffic Violation Factors in Vehicle Insurance Pricing Based on Generalized Linear Model." In International Conference on Innovations in Applied Mathematics, Physics and Astronomy. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012993100004601.

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Aravindhan, M., and D. Manikavelan. "Improving Stock Market Close Price Prediction: A Generalized Linear Model Approach for NSE Banking Stocks." In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT). IEEE, 2024. https://doi.org/10.1109/gcat62922.2024.10923875.

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Okuno, Alex, and Alberto Ferreira. "Generalized linear tree: a flexible algorithm for predicting continuous variables." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai2021072420.

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Tree-based models are popular among regression methods to predict continuous variables. Also, Generalized Linear Models (GLMs) are pretty standard in many statistical applications and provide a generalization to many of the most commonly applied statistical procedures. However, in most regression tree methods, there is only one theoretical model associated for prediction in the final nodes, like multiple linear regression, logistic regressions, polynomial models, Poisson models, among others. We, therefore, propose a new tree method in which we estimate a GLM in each leaf node of the estimated
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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}. 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 adop
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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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Reports on the topic "Generalized linear model (GLM)"

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Armstrong, Dave. Generalized Linear Models for Social and Health Sciences. Instats Inc., 2023. http://dx.doi.org/10.61700/dpngncc99f4pr469.

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This 12-week workshop provides a comprehensive understanding of GLMs and their application in various social and health science disciplines. With a lecture overview and hands-on lab component for each week, participants will gain practical experience in using R for implementing GLMs, evaluating model fit and presenting model results. An official Instats certificate of completion and 3 ECTS Equivalent points are provided at the conclusion of the seminar.
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Mathew, Sonu, Srinivas S. Pulugurtha, and Sarvani Duvvuri. Modeling and Predicting Geospatial Teen Crash Frequency. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2119.

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This research project 1) evaluates the effect of road network, demographic, and land use characteristics on road crashes involving teen drivers, and, 2) develops and compares the predictability of local and global regression models in estimating teen crash frequency. The team considered data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA for the evaluation and obtained data related to teen crashes from the Highway Safety Information System (HSIS) database. The team extracted demographic and land use characteristics using two different buffer widths (0.25
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Moral, Rafael. Introduction to Generalized Linear Models. Instats Inc., 2024. http://dx.doi.org/10.61700/vteee3zjf6fsm1478.

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This seminar provides a comprehensive introduction to Generalized Linear Models (GLMs), covering binary, binomial, categorical logistic regression, Poisson regression, and advanced topics like overdispersion and zero-inflated models. Participants will gain theoretical knowledge and practical skills in applying GLMs using R, enhancing their ability to perform rigorous statistical analyses in various research scenarios.
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Kwon, Jaymin, Yushin Ahn, and Steve Cheung. Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2022): Data-Driven Exposure Analysis by Transportation Modes. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2220.

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Particulate matter (PM) pollution poses significant health risks, influenced by various meteorological factors and seasonal variations. This study investigates the impact of temperature and other meteorological variables on PM10 and PM2.5 levels in Fresno County, known for high air pollution. Multiple linear regression (MLR) and generalized additive models (GAMs) assess the significance of these relationships. Analyzing data from Fresno County, we examine PM10 and PM2.5 levels across "hot" (June to August) and "cool" (September to May) seasons. Findings indicate PM10, both MLR and GAM models i
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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. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada547012.

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Eberlein, Robert, and Sven Düzel. Fatigue lifetime analysis of POM gears for generalized tooth root shapes. Universidad de los Andes, 2024. https://doi.org/10.51573/andes.pps39.gs.ms.1.

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The current calculation methods for determining the tooth root load capacity of polymer gears (e.g., VDI 2736) are based on the same assumptions as those for steel gears. However, due to the non-linear material behavior, temperature, and rate dependency of polymers, these predictions are often inaccurate. A previous study employed rate-dependent nonlinear viscoplastic finite element (FE) modelling of polyoxymethylene (POM) to quantify material influences not considered in standard metal gear assumptions. A lifetime model was developed and validated to predict tooth root fracture based on rotat
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Comola, Margherita, Rokhaya Dieye, and Bernard Fortin. Heterogeneous peer effects and gender-based interventions for teenage obesity. CIRANO, 2022. http://dx.doi.org/10.54932/tqag9043.

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This paper explores the role of gender heterogeneity in the social diffusion of obesity among adolescents and its policy implications. We propose a generalized linear social interaction model which allows for gender-dependent heterogeneity in peer effects through the channel of social synergy. We estimate the model using data on adolescent Body Mass Index and network-based interactions. Our results show that peer effects are gender-dependent, and male students are particularly responsive to the weight of their female friends. Our simulations indicate that female-tailored interventions are like
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Petrova, Katerina. On the Validity of Classical and Bayesian DSGE-Based Inference. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1084.

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This paper studies large sample classical and Bayesian inference in a prototypical linear DSGE model and demonstrates that inference on the structural parameters based on a Gaussian likelihood is unaffected by departures from Gaussianity of the structural shocks. This surprising result is due to a cancellation in the asymptotic variance resulting into a generalized information equality for the block corresponding to the structural parameters. The underlying reason for the cancellation is the certainty equivalence property of the linear rational expectation model. The main implication of this r
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Coscia, Jordan, Wendy Cass, and Leighton J. Evaluation of 20 years of vegetation change in the Big Meadows landscape of Shenandoah National Park in response to mowing and burning: Application of historic data to identify best practices for future meadow management. National Park Service, 2025. https://doi.org/10.36967/2312599.

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The Big Meadows landscape is an open habitat that supports unique biodiversity not found elsewhere in Shenandoah National Park. To support this biodiversity and to maintain the Meadow’s historic landscape characteristics, Park staff have managed the meadows using prescribed burns, mowing, and the targeted cutting and removal of woody plants since 1998. To assess the impacts of these management actions on the plant community in Big Meadows and to guide future land management recommendations, we visualized changes in the plant community composition between 1998 and 2022 using non-metric multidim
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Tummaruk, Padet, Kakanang Buranaamnuay, Wanpen Adulyanubap, Jinda Singlor, and Mongkol Techakumphu. Development of techniques for freezing boar semen. Chulalongkorn University, 2006. https://doi.org/10.58837/chula.res.2006.91.

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The objective of the present study was to investigate the feasibility of boar semen cryopreservation in Thailand and to investigate factors that could affect the boar semen quality after cryopreservation. Fifteen purebred boars (5 Landrace, 5 Yorkshire and 5 Duroc) from 2 commercial herds in Nakorn-prathom province were used in the experiment. Three ejaculates from each boar were collected with over one-week interval using gloved-hand technique. After collection, the semen was diluted with isothermal Beltsville thawing solution (BTS) extender. Diluted semen was held at 15 °C for 2 h and centri
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