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Chi-Square Orthogonal Components for Assessing Goodness-of-fit of Multidimensional Multinomial Data


Abstract It is common in the analysis of data to provide a goodness-of-fit test to assess the performance of a model. In the analysis of contingency tables, goodness-of-fit statistics are frequently employed when modeling social science, educational or psychological data where the interest is often directed at investigating the association among multi-categorical variables. Pearson's chi-squared statistic is well-known in goodness-of-fit testing, but it is sometimes considered to produce an omnibus test as it gives little guidance to the source of poor fit once the null hypothesis is rejected. However, its components can provide powerful directional tests. In this dissertation, orthogonal components are used to develop goodness-of-fit tests for ... (more)
Created Date 2011
Contributor Milovanovic, Jelena (Author) / Young, Dennis (Advisor) / Reiser, Mark (Advisor) / Wilson, Jeffrey (Committee member) / Eubank, Randall (Committee member) / Yang, Yan (Committee member) / Arizona State University (Publisher)
Subject Statistics / Chi-Square goodness-of-fit tests / decomposition of chi-square statistic / Orthogonal components of chi-square statistic
Type Doctoral Dissertation
Extent 217 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Ph.D. Mathematics 2011
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis