Small-Sample Comparisons for Goodness-of-Fit Statistics in One-Way Multinomials with Composite Hypotheses

ISSN： 1360-0532

Source： Journal of Applied Statistics, Vol.31, Iss.2, 2004-02, pp. : 161-181

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Abstract

The small-sample behaviour of power-divergence goodness-of-fit statistics with composite hypotheses was evaluated with multinomial models of up to five cells and up to three parameters. Their performance was assessed by comparing asymptotic test sizes with exact test sizes obtained by enumeration in the near right tail, where 1- isin (0.90, 0.95], and in the far right tail, where 1- isin (0.95, 0.99]. The study addressed all combinations of power-diparse JAS312HH01.sgmvergence estimates of indices ngr isin {-1/2, 0, 1/3, 1/2, 2/3, 1, 3/ 2} and power-divergence statistics of indices lgr isin {-1/2, 0, 1/3, 1/2, 2/3, 1, 3/2}. The results indicate that the asymptotic approximation is sufficiently accurate (by the criterion that the average exact size is no larger than ±10% of the nominal asymptotic test size) in the near right tail when ngr =0 and lgr =1/2, and in the far right tail when ngr =0 and lgr =1/3, in both cases providing that the smallest expectation in the composite hypothesis exceeds 5. The only exception to this rule is the case of models that render a near-equiprobable composite hypothesis on the boundaries of the parameter space, where average exact sizes are usually quite different from nominal sizes despite the fact that the smallest expectation in these conditions is usually well above 5.