Maximum likelihood analysis of partially-classified contingency tables via the EM algorithm
Date of Publication
1994
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Mathematics
College
College of Science
Department/Unit
Mathematics and Statistics
Abstract/Summary
Methods for the analysis of fully-classified contingency tables are very well known, especially among social science practitioners. However, certain process of collecting data may yield some observations which do not clearly fall under any of the underlying categories and hence result in partially-classified tables. Such situations arise frequently in practice, but ironically, the techniques to handle them are not so well known.This thesis presents an exposition on the methods to handle such data. In particular, this thesis discusses the analysis of partially-classified contingency tables when the process that leads to the nonresponse is ignorable. The discussion is mainly based on Fuch (1982) and Little and Rubin (1987).
Abstract Format
html
Language
English
Format
Accession Number
TU06644
Shelf Location
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
Physical Description
[83] leaves
Keywords
Analysis of variance; Algorithms; Contingency tables; Distribution (Probability theory); Mathematical statistics; Programming (Mathematics)
Recommended Citation
Bautista, O. M. (1994). Maximum likelihood analysis of partially-classified contingency tables via the EM algorithm. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/16163
