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Elastic Net Regularization Paths for All Generalized Linear Models. [PDF]
J Stat Softw, 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 ...
Tay JK, Narasimhan B, Hastie T.
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Feature Screening for High-Dimensional Variable Selection in Generalized Linear Models. [PDF]
Entropy (Basel), 2023 The two-stage feature screening method for linear models applies dimension reduction at first stage to screen out nuisance features and dramatically reduce the dimension to a moderate size; at the second stage, penalized methods such as LASSO and SCAD ...
Jiang J, Shang J.
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Generalized Linear Models with Covariate Measurement Error and Zero-Inflated Surrogates. [PDF]
Mathematics (Basel)Epidemiological studies often encounter a challenge due to exposure measurement error when estimating an exposure–disease association. A surrogate variable may be available for the true unobserved exposure variable.
Wang CY +3 more
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partR2: partitioning R2 in generalized linear mixed models [PDF]
PeerJ, 2021 The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by ...
Martin A. Stoffel +2 more
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Transfer Learning under High-dimensional Generalized Linear Models. [PDF]
J Am Stat Assoc, 2023 Tian Y, Feng Y.
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Bias-Corrected Inference of High-Dimensional Generalized Linear Models
Mathematics, 2023 In this paper, we propose a weighted link-specific (WLS) approach that establishes a unified statistical inference framework for high-dimensional Poisson and Gamma regression.
Shengfei Tang, Yanmei Shi, Qi Zhang
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CytoGLMM: conditional differential analysis for flow and mass cytometry experiments
BMC Bioinformatics, 2021 Background Flow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells. The goal is to better understand the mechanisms of responses on a single cell basis by studying differential ...
Christof Seiler +7 more
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Semina: Ciências Exatas e Tecnológicas, 2021 Generalized additive models for location, scale and shape (GAMLSS) are a very flexible statistical modeling framework, being an important generalization of the well-known generalized linear models and generalized additive models.
Fernanda V. Roquim +5 more
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Analysis of Robust Quasi-deviances for Generalized Linear Models
Journal of Statistical Software, 2004 Generalized linear models (McCullagh and Nelder 1989) are a popular technique for modeling a large variety of continuous and discrete data. They assume that the response variables Yi , for i = 1, . . .
Eva Cantoni
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ParMA: Parallelized Bayesian Model Averaging for Generalized Linear Models
Journal of Statistical Software, 2022 This paper describes the gretl function package ParMA, which provides Bayesian model averaging (BMA) in generalized linear models. In order to overcome the lack of analytical specification for many of the models covered, the package features an ...
Riccardo (Jack) Lucchetti, Luca Pedini
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