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• W160347941 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 chisquared 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 models fit to the counts obtained from the crossclassification of multi-category dependent variables. Ordinal categories are assumed. Orthogonal components defined on marginals are obtained when analyzing multi-dimensional contingency tables through the use of the QR decomposition. A subset of these orthogonal components can be used to construct limited-information tests that allow one to identify the source of lack-offit and provide an increase in power compared to Pearson’s test. These tests can address the adverse effects presented when data are sparse. The tests rely on the set of firstand second-order marginals jointly, the set of second-order marginals only, and the random forest method, a popular algorithm for modeling large complex data sets. The performance of these tests is compared to the likelihood ratio test as well as to tests based on orthogonal polynomial components." @default.
• W160347941 created "2016-06-24" @default.
• W160347941 creator A5062928679 @default.
• W160347941 date "2011-01-01" @default.
• W160347941 modified "2023-09-27" @default.
• W160347941 title "Chi-Square Orthogonal Components for Assessing Goodness-of-fit of Multidimensional Multinomial Data" @default.
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• W160347941 hasPublicationYear "2011" @default.
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