A Look at Various Estimators in Logistic Models in the Presence of Missing Values
Abstract
Two commonly used procedures for estimating the parameters of a logistic regression function are the maximum likelihood estimators and the discriminant function estimators. Comparisons of these procedures for fitting logistic regression models based on the experience of many researchers can be found in the literature. The comparisons become more complicated when one or more values of the independent variables of certain observations are missing at random. When data are missing, researchers may not be willing to base their estimates only on the subset of complete cases, particularly if the size of this subset is relatively small.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 1979
- Accession Number
- ADA483877
Entities
People
- Winston K. Chow
Organizations
- RAND Corporation