Shrinkage and Penalty Estimators of a Poisson Regression Model

DOI10.1111/j.1467-842X.2012.00679.xzbMath1334.62127MaRDI QIDQ2802803

Shakhawat Hossain, S. Ejaz Ahmed

Publication date: 27 April 2016

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

zbMATH Keywords

likelihood ratio testMonte Carlo simulationPoisson regressionshrinkage estimatorsasymptotic distributional bias and riskpenalty estimators

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) General nonlinear regression (62J02)

Related Items (8)

A comparison of preliminary test, Stein-type and penalty estimators in gamma regression model ⋮ On the James-Stein estimator for the poisson regression model ⋮ Shrinkage estimation of the exponentiated Weibull regression model for time‐to‐event data ⋮ Penalised, post‐pretest, and post‐shrinkage strategies in nonlinear growth models ⋮ Improved shrinkage estimators in the beta regression model with application in econometric and educational data ⋮ Penalized and Shrinkage Estimation in the Cox Proportional Hazards Model ⋮ Model selection and parameter estimation of a multinomial logistic regression model ⋮ Model selection and post estimation based on a pretest for logistic regression models

Uses Software

• glmnet

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• LASSO and shrinkage estimation in Weibull censored regression models
• A local maximum likelihood estimator for Poisson regression
• Positive shrinkage, improved pretest and absolute penalty estimators in partially linear models
• Least angle regression. (With discussion)
• Risk comparison of some shrinkage M-estimators in linear models
• Applied Linear Models with SAS
• SHRINKAGE, PRETEST AND ABSOLUTE PENALTY ESTIMATORS IN PARTIALLY LINEAR MODELS
• Unified LASSO Estimation by Least Squares Approximation
• ESTIMATION OF REGRESSION COEFFICIENTS IN AN EXPONENTIAL REGRESSION MODEL WITH CENSORED OBSERVATIONS
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparsity and Smoothness Via the Fused Lasso
• Smoothly Clipped Absolute Deviation on High Dimensions
• Model Selection and Estimation in Regression with Grouped Variables
• A Semiparametric Basis for Combining Estimation Problems Under Quadratic Loss

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