introduced a class of multinomial goodness-of-fit statistics R a based on power divergence. All R a have the same chi-square limiting distribution under null hypothesis and have the same noncentral chi-square limiting distribution under local alternatives. In this paper, we investigate asymptotic ap
Asymptotic distributions for goodness-of-fit statistics in a sequence of multinomial models
✍ Scribed by L. Györfi; I. Vajda
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 130 KB
- Volume
- 56
- Category
- Article
- ISSN
- 0167-7152
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✦ Synopsis
We consider f-disparities D f ( pn ; p n ) between discrete distributions p n = (p n1 ; : : : ; p nkn ) and their estimates pn = ( pn1 ; : : : ; pnkn ) based on relative frequencies in an i.i.d. sample of size n, where f : (0; ∞) → R is twice continuously di erentiable in a neighborhood of 1 with f (1) = 0. We derive asymptotic distributions of the disparity statistics n D f ( pn ; p n ) under certain assumptions about p n and the second derivatives f in a neighborhood of 1. These assumptions are weaker than those known from the literature.
📜 SIMILAR VOLUMES
Cressie and Read (J. Roy. Statist. Soc. B 46 (1984) introduced the power divergence statistics, R a ; as multinomial goodness-of-fit statistics. Each R a has a limiting noncentral chi-square distribution under a local alternative and has a limiting normal distribution under a nonlocal alternative. T
This paper investigates a new family of statistics based on Burbea Rao divergence for testing goodness-of-fit. Under the simple and composite null hypotheses the asymptotic distribution of these tests is shown to be chi-squared. For composite hypothesis, the unspecified parameters are estimated by m