penalized: A MATLAB Toolbox for Fitting Generalized Linear Models with Penalties [PDF]
Journal of Statistical Software, 2016 penalized is a flexible, extensible, and efficient MATLAB toolbox for penalized maximum likelihood. penalized allows you to fit a generalized linear model (gaussian, logistic, poisson, or multinomial) using any of ten provided penalties, or none.
William McIlhagga
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The Estimation of Item Response Models with the lmer Function from the lme4 Package in R [PDF]
Journal of Statistical Software, 2011 In this paper we elaborate on the potential of the lmer function from the lme4 package in R for item response (IRT) modeling. In line with the package, an IRT framework is described based on generalized linear mixed modeling. The aspects of the framework
Paul De Boeck +6 more
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Regularization Paths for Generalized Linear Models via Coordinate Descent [PDF]
Journal of Statistical Software, 2010 We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge
Jerome Friedman +2 more
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Modelos de predição para sobrevivência de plantas de Eucalyptus grandis Prediction models of Eucalyptus grandis plant survival [PDF]
Ciência e Agrotecnologia, 2009 Objetivou-se com este trabalho comparar modelos de predição de plantas sobreviventes de Eucalyptus grandis. Utilizaram-se os seguintes modelos: modelo linear misto com os dados transformados, utilizando-se as transformações angular e BOX-COX; modelo ...
Telde Natel Custódio, Décio Barbin
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In Search of Complex Disease Risk through Genome Wide Association Studies
Mathematics, 2021 The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research.
Lorena Alonso +3 more
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Elastic Net Regularization Paths for All Generalized Linear Models
Journal of Statistical Software, 2023 The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient algorithm for computing the elastic net regularization path for ordinary least ...
J. Kenneth Tay +2 more
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PLoS ONE, 2021 Binary Generalized Linear Mixed Model (GLMM) is the most common method used by researchers to analyze clustered binary data in biological and social sciences.
Chénangnon Frédéric Tovissodé +2 more
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Using Multinomial Logistic Regression model to study factors that affect chest pain
Tikrit Journal of Administrative and Economic Sciences, 2021 In this work Logistic Regression model was utilized which is one of the significant techniques for categorical data analysis, the purpose of this study to distinguish a use of Multinomial Logistic Regression method when model arrangements one (
Samira Muhamad Salh +2 more
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Generalized linear mixed models can detect unimodal species-environment relationships [PDF]
PeerJ, 2013 Niche theory predicts that species occurrence and abundance show non-linear, unimodal relationships with respect to environmental gradients. Unimodal models, such as the Gaussian (logistic) model, are however more difficult to fit to data than linear ...
Tahira Jamil, Cajo J.F. ter Braak
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Comparison of Logistic Regression and Generalized Linear Model for Identifying Accurate At – Risk Students [PDF]
Alinteri Journal of Agriculture Sciences, 2021 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.
K. Harini, K. Sashi Rekha
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