Logistic regression with a partially observed covariate
- 1 January 1989
- journal article
- research article
- Published by Taylor & Francis in Communications in Statistics - Simulation and Computation
- Vol. 18 (1) , 163-177
- https://doi.org/10.1080/03610918908812752
Abstract
We present results of a Monte Carlo study comparing four methods of estimating the parameters of the logistic model logit (pr (Y = 1 | X, Z)) = α0 + α 1 X + α 2 Z where X and Z are continuous covariates and X is always observed but Z is sometimes missing. The four methods examined are 1) logistic regression using complete cases, 2) logistic regression with filled-in values of Z obtained from the regression of Z on X and Y, 3) logistic regression with filled-in values of Z and random error added, and 4) maximum likelihood estimation assuming the distribution of Z given X and Y is normal. Effects of different percent missing for Z and different missing value mechanisms on the bias and mean absolute deviation of the estimators are examined for data sets of N = 200 and N = 400.Keywords
This publication has 7 references indexed in Scilit:
- Maximum likelihood estimation for mixed continuous and categorical data with missing valuesBiometrika, 1985
- Maximum Likelihood Estimation and Model Selection in Contingency Tables with Missing DataJournal of the American Statistical Association, 1982
- Models for Nonresponse in Sample SurveysJournal of the American Statistical Association, 1982
- Maximum Likelihood from Incomplete Data Via the EM AlgorithmJournal of the Royal Statistical Society Series B: Statistical Methodology, 1977
- Inference and missing dataBiometrika, 1976
- Characterizing the Estimation of Parameters in Incomplete-Data ProblemsJournal of the American Statistical Association, 1974
- Multivariate Correlation Models with Mixed Discrete and Continuous VariablesThe Annals of Mathematical Statistics, 1961