Logistic regression with missing values in the covariates
type: Item
instance of: scholarly article
title: Logistic regression with missing values in the covariates
zbMATH Open document ID: 0801.62061
author: Werner Vach
published in: Lecture Notes in Statistics
publication date: 1994-06-26T00:00:00Z
review text: Assume logistic regression of the binary variable \(Y\) on covariates \(X_ 1\) and \(X_ 2\) where missing values of \(X_ 2\) can occur. MAR and MDX state, that the probability of missing \(X_ 2\) depends, respectively, only on the values of \(Y\) and \(X_ 1\) and only on the value of \(X_ 1\). Maximum likelihood estimation is studied under MAR. Pseudo maximum likelihood estimation, involving replacement of a nuisance parameter by a consistent estimate, is studied under \(MDX\). Finally, under MAR, the filling method is considered. Here missing values are estimated prior to using maximum likelihood estimates. The main evaluation of procedures is asymptotic with only simulation studies for finite sample sizes. Finally, some more general models are discussed.
Mathematics Subject Classification ID: 62J99
Mathematics Subject Classification ID: 62-02
zbMATH DE Number: 596193
zbMATH Keywords: missing covariate values
zbMATH Keywords: asymptotic relative efficiency
zbMATH Keywords: asymptotic bias
zbMATH Keywords: pseudo maximum likelihood estimation
zbMATH Keywords: logistic regression
zbMATH Keywords: MAR
zbMATH Keywords: replacement of nuisance parameter
zbMATH Keywords: filling method
zbMATH Keywords: maximum likelihood estimates
zbMATH Keywords: simulation studies for finite sample sizes
reviewed by: Martin Fox
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 596193