Cover: A Look at Various Estimators in Logistic Models in the Presence of Missing Values

A Look at Various Estimators in Logistic Models in the Presence of Missing Values

Published 1979

by Winston Chow


Download eBook for Free

FormatFile SizeNotes
PDF file 0.6 MB

Use Adobe Acrobat Reader version 10 or higher for the best experience.


Purchase Print Copy

 Format Price
Add to Cart Paperback27 pages $20.00

Two commonly used procedures for estimating the parameters of a logistic regression function are the maximum likelihood estimators and the discriminant function estimators. When data are missing, researchers may not be willing to base their estimates on the subset of complete cases. This paper describes four modifications of these procedures for handling the missing-values case. One modification of the discriminant function estimators involves estimating the sample means and correlations from the complete pairs of observed values. An alternative procedure involves replacing the missing entries by zeros and augmenting the logistic regression model with indicator variables for the missing values. Two other modifications require replacing the missing entries by some fitted values based on all other available information. The resulting approximation errors are then accounted for in the covariance structure of the observations.

This report is part of the RAND note series. The note was a product of RAND from 1979 to 1993 that reported other outputs of sponsored research for general distribution.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.