Testing for Independence on statistically matched categorical variables

In most instances, conducting a new survey is impossible due to time constraints and limited resources. Matching data sources has been used as a way to obtain a data set where all the intended variables are available. This paper proposes the use of the MCMC and the inclusion of random error in match...

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Main Author:	De Veyra, Janna M.
Format:	text
Published:	Animo Repository 2019
Subjects:	Markov processes Errors-in-variables models Social and Behavioral Sciences
Online Access:	https://animorepository.dlsu.edu.ph/faculty_research/11187
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Institution:	De La Salle University

id	oai:animorepository.dlsu.edu.ph:faculty_research-11601
record_format	eprints
spelling	oai:animorepository.dlsu.edu.ph:faculty_research-116012023-10-28T01:07:54Z Testing for Independence on statistically matched categorical variables De Veyra, Janna M. In most instances, conducting a new survey is impossible due to time constraints and limited resources. Matching data sources has been used as a way to obtain a data set where all the intended variables are available. This paper proposes the use of the MCMC and the inclusion of random error in matching categorical variables as well as the application of bootstrap procedure in testing for their independence. A simulation study indicates that the test is most effective when the proposed procedures are all applied because combining all these procedures produces a correctly sized test that yields the highest power among all other proposed procedures combined. 2019-08-01T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/11187 Faculty Research Work Animo Repository Markov processes Errors-in-variables models Social and Behavioral Sciences
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
topic	Markov processes Errors-in-variables models Social and Behavioral Sciences
spellingShingle	Markov processes Errors-in-variables models Social and Behavioral Sciences De Veyra, Janna M. Testing for Independence on statistically matched categorical variables
description	In most instances, conducting a new survey is impossible due to time constraints and limited resources. Matching data sources has been used as a way to obtain a data set where all the intended variables are available. This paper proposes the use of the MCMC and the inclusion of random error in matching categorical variables as well as the application of bootstrap procedure in testing for their independence. A simulation study indicates that the test is most effective when the proposed procedures are all applied because combining all these procedures produces a correctly sized test that yields the highest power among all other proposed procedures combined.
format	text
author	De Veyra, Janna M.
author_facet	De Veyra, Janna M.
author_sort	De Veyra, Janna M.
title	Testing for Independence on statistically matched categorical variables
title_short	Testing for Independence on statistically matched categorical variables
title_full	Testing for Independence on statistically matched categorical variables
title_fullStr	Testing for Independence on statistically matched categorical variables
title_full_unstemmed	Testing for Independence on statistically matched categorical variables
title_sort	testing for independence on statistically matched categorical variables
publisher	Animo Repository
publishDate	2019
url	https://animorepository.dlsu.edu.ph/faculty_research/11187
_version_	1781418224241344512

Testing for Independence on statistically matched categorical variables

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