Results 21 to 30 of about 1,282,217 (275)
Boosting Correlation Based Penalization in Generalized Linear Models [PDF]
, 2007 In high dimensional regression problems penalization techniques are a useful tool for estimation and variable selection. We propose a novel penalization technique that aims at the grouping effect which encourages strongly correlated predictors to be in ...
Tutz, Gerhard, Ulbricht, Jan
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Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation [PDF]
, 2011 Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates.
Groll, Andreas
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Sensitivity analysis for causal effects with generalized linear models
Journal of Causal Inference, 2022 Residual confounding is a common source of bias in observational studies. In this article, we build upon a series of sensitivity analyses methods for residual confounding developed by Brumback et al. and Chiba whose sensitivity parameters are constructed
Sjölander Arvid +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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Generalized fiducial inference for normal linear mixed models [PDF]
, 2012 While linear mixed modeling methods are foundational concepts introduced in any statistical education, adequate general methods for interval estimation involving models with more than a few variance components are lacking, especially in the unbalanced ...
Cisewski, Jessi, Hannig, Jan
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Using R In Generalized Linear Models [PDF]
Revista Română de Statistică, 2015 This paper aims to approach the estimation of generalized linear models (GLM) on the basis of the glm routine package in R. Particularly, regression models will be analyzed for those cases in which the explained variable follows a Poisson or a Negative ...
Mihaela Covrig +4 more
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Mathematics, 2023 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 ...
Zuleyka Díaz Martínez +2 more
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Albatross analytics a hands-on into practice: statistical and data science application
Journal of Big Data, 2022 Albatross Analytics is a statistical and data science data processing platform that researchers can use in disciplines of various fields. Albatross Analytics makes it easy to implement fundamental analysis for various regressions with random model ...
Rezzy Eko Caraka +7 more
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Pyglmnet : Python implementation of elastic-net regularized generalized linear models [PDF]
, 2020 Graceful handling of small Hessian term in coordinate descent solver that led to exploding update term Ensure full compatibility of GLM class with scikit ...
Achakulvisut, Titipat +21 more
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Holistic Generalized Linear Models
Journal of Statistical SoftwareHolistic linear regression extends the classical best subset selection problem by adding additional constraints designed to improve the model quality.
Benjamin Schwendinger +2 more
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