Model validation and influence diagnostics for regression models with missing covariates

Statistics in Medicine
Paul W Bernhardt

Abstract

Missing covariate values are prevalent in regression applications. While an array of methods have been developed for estimating parameters in regression models with missing covariate data for a variety of response types, minimal focus has been given to validation of the response model and influence diagnostics. Previous research has mainly focused on estimating residuals for observations with missing covariates using expected values, after which specialized techniques are needed to conduct proper inference. We suggest a multiple imputation strategy that allows for the use of standard methods for residual analyses on the imputed data sets or a stacked data set. We demonstrate the suggested multiple imputation method by analyzing the Sleep in Mammals data in the context of a linear regression model and the New York Social Indicators Status data with a logistic regression model.

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Citations

Nov 1, 2018·The International Journal of Biostatistics·Paul W Bernhardt
Nov 30, 2019·Statistical Methods in Medical Research·Daiane A ZuanettiLuis A Milan
Oct 28, 2019·The Journal of Clinical Endocrinology and Metabolism·Lihua HuXiaoshu Cheng
Apr 2, 2021·Infectious Diseases of Poverty·Hong ZhaoGui-Qiang Wang

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