Approximate Maximum Likelihood Estimates in Regression Models for Grouped Data
Abstract
Grouped data arise quite frequently in application. Various experimental situations naturally lead to observations classified into certain groups. The process of recording or storing observations directly leads to grouped data. Other examples of circumstances which lead to grouped data have recently been discussed by Haitovsky (1973) in his monograph on regression estimation from grouped observations. From a practical point of view, the case for grouped data has been advocated by Durvin (1954) as a technique for minimizing errors of measurement. Grouped observations also result in experiments where precise measuring instruments are not available.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1978
- Accession Number
- ADA062926
Entities
People
- A. Indrayan
- J. S. Rustagi
Organizations
- Ohio State University