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.

A Power study of the GFfit statistic as a Lack-of-fit Diagnostic for sparse two-way subtables / Junfei Zhu; Mark Reiser; Maduranga Dassanayake; Silvia, Cagnone. - ELETTRONICO. - (2016), pp. 2401-2410. (Intervento presentato al convegno 2016 Joint Statistical Meetings tenutosi a Chicago nel July 30- August 4, 2016).

A Power study of the GFfit statistic as a Lack-of-fit Diagnostic for sparse two-way subtables

CAGNONE, SILVIA
2016

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. 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.
2016
2016 JSM proceedings : papers presented at the Joint Statistical Meetings, Chicago,Illinois, July 30- August 4, 2016: and other ASA-sponsored conferences
2401
2410
A Power study of the GFfit statistic as a Lack-of-fit Diagnostic for sparse two-way subtables / Junfei Zhu; Mark Reiser; Maduranga Dassanayake; Silvia, Cagnone. - ELETTRONICO. - (2016), pp. 2401-2410. (Intervento presentato al convegno 2016 Joint Statistical Meetings tenutosi a Chicago nel July 30- August 4, 2016).
Junfei Zhu; Mark Reiser; Maduranga Dassanayake; Silvia, Cagnone
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/582729
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