Online Program Home
My Program

Abstract Details

Activity Number: 699
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #319210
Title: Power Calculations for Statistics Based on Orthogonal Components of Pearson's Chi-Square
Author(s): Maduranga Dassanayake* and Mark Reiser
Companies: Arizona State University and Arizona State University
Keywords: Item response model ; Asymptotic power ; Orthogonal components ; Monte Carlo simulation

The Pearson and likelihood ratio statistics are commonly used to test goodness of fit for models applied to count data from a multinomial distribution. When counts are from a table formed by the cross classification of a large number of variables, the traditional statistics may have lower power and inaccurate Type I error level due to sparseness. For a cross-classified table, Pearson's statistic can be decomposed into orthogonal components associated with the marginal distribution of observed variables, and an omnibus fit statistic defined on a sum of components for lower-order marginals has good performance for Type I error rate and statistical power, even when applied to a sparse table. In this study asymptotic power will be calculated for statistics based on orthogonal components and will be compared to results obtained by using Monte Carlo simulations. Power will be calculated for testing a confirmatory dichotomous variable factor analysis model and will be investigated for both individual components that can serve as lack-of-fit diagnostics and for omnibus statistics formed by summing orthogonal components.

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

Back to the full JSM 2016 program

Copyright © American Statistical Association