Abstract
Abstract Complex sample designs typically invalidate the direct application of the familiar Pearson or likelihood-ratio chi-squared statistics for testing the fit of a model to a cross-classified table of counts. This article discusses the adjustment of these statistics through a jackknifing approach. The technique may generally be applied whenever a standard replication method, such as the jackknife, bootstrap, or repeated half-samples, provides a consistent estimate of the covariance matrix of the sample estimates. Properties of the limiting distribution of new test statistics, Xj and GJ , are described. The new statistics may be used to test goodness of fit and to compare nested models.
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