Model selection and post estimation based on a pretest for logistic regression models

DOI10.1080/00949655.2016.1167894OpenAlexW2332977150MaRDI QIDQ5221548

Supranee Lisawadi, Muhammad Kashif Ali Shah, S. Ejaz Ahmed

Publication date: 1 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2016.1167894

zbMATH Keywords

linear shrinkage estimatorasymptotic distributional biaspreliminary test estimatorasymptotic quadratic risksubspace information

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Point estimation (62F10)

Related Items (8)

A comparison of preliminary test, Stein-type and penalty estimators in gamma regression model ⋮ Penalty, post pretest and shrinkage strategies in a partially linear 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 ⋮ Pretest and shrinkage estimation of the regression parameter vector of the marginal model with multinomial responses ⋮ A sufficient condition for the MSE dominance of the positive-part shrinkage estimator when each individual regression coefficient is estimated in a misspecified linear regression model ⋮ Adaptive estimation strategies in gamma regression model ⋮ Ridge-type shrinkage estimators in generalized linear models with an application to prostate cancer data

Cites Work

• Asymptotic properties of the maximum likelihood estimator in dichotomous logit models
• Penalty, shrinkage and pretest strategies. Variable selection and estimation
• Shrinkage and Penalty Estimators of a Poisson Regression Model
• On Biases in Estimation Due to the Use of Preliminary Tests of Significance
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