Testing for Independence on statistically matched categorical variables

College

College of Science

Document Type

Conference Proceeding

Source Title

62nd ISI World Statistics Congress 2019

First Page

354

Last Page

360

Publication Date

8-2019

Abstract

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.

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Disciplines

Social and Behavioral Sciences

Keywords

Markov processes; Errors-in-variables models

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