Maximum likelihood analysis of partially-classified contingency tables via the EM algorithm

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-...

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Main Author: Bautista, Olivar M.
Format: text
Language:English
Published: Animo Repository 1994
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Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/16163
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-166762021-11-13T03:54:02Z Maximum likelihood analysis of partially-classified contingency tables via the EM algorithm Bautista, Olivar M. 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). 1994-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/16163 Bachelor's Theses English Animo Repository Analysis of variance Algorithms Contingency tables Distribution (Probability theory) Mathematical statistics Programming (Mathematics)
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Analysis of variance
Algorithms
Contingency tables
Distribution (Probability theory)
Mathematical statistics
Programming (Mathematics)
spellingShingle Analysis of variance
Algorithms
Contingency tables
Distribution (Probability theory)
Mathematical statistics
Programming (Mathematics)
Bautista, Olivar M.
Maximum likelihood analysis of partially-classified contingency tables via the EM algorithm
description 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).
format text
author Bautista, Olivar M.
author_facet Bautista, Olivar M.
author_sort Bautista, Olivar M.
title Maximum likelihood analysis of partially-classified contingency tables via the EM algorithm
title_short Maximum likelihood analysis of partially-classified contingency tables via the EM algorithm
title_full Maximum likelihood analysis of partially-classified contingency tables via the EM algorithm
title_fullStr Maximum likelihood analysis of partially-classified contingency tables via the EM algorithm
title_full_unstemmed Maximum likelihood analysis of partially-classified contingency tables via the EM algorithm
title_sort maximum likelihood analysis of partially-classified contingency tables via the em algorithm
publisher Animo Repository
publishDate 1994
url https://animorepository.dlsu.edu.ph/etd_bachelors/16163
_version_ 1772835060031946752