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A Power Study of Gffit Statistics as Components of Pearson Chi-Square


Abstract The Pearson and likelihood ratio statistics are commonly used to test goodness-of-fit for models applied to data from a multinomial distribution. When data are from a table formed by cross-classification of a large number of variables, the common statistics may have low power and inaccurate Type I error level due to sparseness in the cells of the table. The GFfit statistic can be used to examine model fit in subtables. It is proposed to assess model fit by using a new version of GFfit statistic based on orthogonal components of Pearson chi-square as a diagnostic to examine the fit on two-way subtables. However, due to variables with a large number of categories and small sample size, even the GFfit statistic may have low power and inaccurat... (more)
Created Date 2017
Contributor Zhu, Junfei (Author) / Reiser, Mark (Advisor) / Stufken, John (Committee member) / Zheng, Yi (Committee member) / St Louis, Robert (Committee member) / Kao, Ming-Hung (Committee member) / Arizona State University (Publisher)
Subject Statistics / goodness-of-fit
Type Doctoral Dissertation
Extent 134 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Statistics 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis