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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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) |
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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 |
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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). |
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Bautista, Olivar M. |
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Bautista, Olivar M. |
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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 |
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Animo Repository |
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1994 |
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https://animorepository.dlsu.edu.ph/etd_bachelors/16163 |
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