Journal article
Noncentrality Parameters in Chi-Squared Goodness-of-Fit Analyses with an Application to Log-Linear Procedures
Journal of the American Statistical Association, v 80(389), pp 181-189
01 Mar 1985
Abstract
Limiting noncentral chi-squared distributions are obtained for statistics commonly used in sequential stepwise testing to select from a general class of functions the correct model for T multinomial cell frequencies in an m-way cross-classification table. The simple quadratic form expressions are used to establish the asymptotic independence of tests of nested models used in stepwise selection. In an application of these results, the quadratic form expression for the noncentrality parameter of the asymptotic distribution of chi-squared statistics testing H
0
versus H
1
, when H
2
is true is used to compare the powers of hierarchical log-linear model-selection procedures. Tables and charts of approximate powers of stepwise testing procedures to select the correct log-linear model for 2 × 2 × 2 and 2 × 3 × 3 cross-classification tables are presented for a range of sample sizes and magnitudes of higher-order effect parameters. The powers of backward elimination and forward selection procedures are compared.
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7 citations in Scopus
Details
- Title
- Noncentrality Parameters in Chi-Squared Goodness-of-Fit Analyses with an Application to Log-Linear Procedures
- Creators
- Jacqueline Oler - Drexel University
- Publication Details
- Journal of the American Statistical Association, v 80(389), pp 181-189
- Publisher
- Taylor & Francis Group
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Scopus ID
- 2-s2.0-0037702583
- Other Identifier
- 991019174148304721