Shrinkage, pretest, and penalty estimators in generalized linear models

DOI10.1016/j.stamet.2014.11.003zbMath1486.62215OpenAlexW2004462092MaRDI QIDQ1731260

Kjell A. Doksum, S. Ejaz Ahmed, Shakhawat Hossain

Publication date: 13 March 2019

Published in: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.stamet.2014.11.003

zbMATH Keywords

generalized linear modelslinear restrictionsSCADasymptotic riskpretestStein type shrinkage\(L_1\)GLMadaptive \(L_1\)GLMcandidate subspacesGLM likelihood ratio test

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic distribution theory in statistics (62E20) Generalized linear models (logistic models) (62J12)

Related Items

A comparison of preliminary test, Stein-type and penalty estimators in gamma regression model, Improved shrinkage estimators in zero-inflated negative binomial regression model, Using shrinkage strategies to estimate fixed effects in zero-inflated negative binomial mixed model, Generalized autoregressive moving average models: an efficient estimation approach, Pretest and shrinkage estimators in generalized partially linear models with application to real data, Shrinkage estimation in linear mixed models for longitudinal data, Shrinkage and penalized estimators in weighted least absolute deviations regression models, Non-penalty shrinkage estimation of random effect models for longitudinal data with AR(1) errors, Unnamed Item, Adaptive estimation strategies in gamma regression model, Estimation strategy of multilevel model for ordinal longitudinal data, Ridge-type shrinkage estimators in generalized linear models with an application to prostate cancer data

Uses Software

• ElemStatLearn

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• LASSO and shrinkage estimation in Weibull censored regression models
• Positive shrinkage, improved pretest and absolute penalty estimators in partially linear models
• Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models
• Least angle regression. (With discussion)
• Estimation for High-Dimensional Linear Mixed-Effects Models Using ℓ1-Penalization
• The Loss Rank Criterion for Variable Selection in Linear Regression Analysis
• Risk comparison of some shrinkage M-estimators in linear models
• The horseshoe estimator for sparse signals
• Unified LASSO Estimation by Least Squares Approximation
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparsity and Smoothness Via the Fused Lasso
• Generalized double Pareto shrinkage
• L 1-Regularization Path Algorithm for Generalized Linear Models
• Smoothly Clipped Absolute Deviation on High Dimensions
• Model Selection and Estimation in Regression with Grouped Variables
• Theory of Preliminary Test and Stein‐Type Estimation With Applications
• A Semiparametric Basis for Combining Estimation Problems Under Quadratic Loss
• The elements of statistical learning. Data mining, inference, and prediction