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Activity Number: 34
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #320066
Title: A Power Study of the GFfit Statistic as a Lack-of-Fit Diagnostic for Sparse Two-Way Subtables
Author(s): Junfei Zhu* and Mark Reiser and Silvia Cagnone
Companies: ASU and Arizona State University and University of Bologna
Keywords: sparseness ; GFfit statistic ; orthogonal components ; chi-square test ; goodness-of-fit

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. It has been 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 inaccurate Type I error level due to sparseness in the two-way subtable. In this paper, a method based on choosing different orthogonal components for the GFfit statistic on the subtables is developed to improve the performance of the GFfit statistic. Simulation results for power and type I error rate for several different cases along with comparisons to other diagnostics are presented.

Authors who are presenting talks have a * after their name.

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