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Goodness-of-fit tests based on Rao's divergence under sparseness assumptions

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Pardo Llorente, María del Carmen (2002) Goodness-of-fit tests based on Rao's divergence under sparseness assumptions. Applied Mathematics and Computation, 130 (2-3). pp. 265-283. ISSN 0096-3003

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Official URL: http://www.sciencedirect.com/science/article/pii/S0096300301000959


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Abstract

In many practical situations the classical (fixed-cells) assumptions to test goodness-of-fit are inappropriate, and we consider an alternative set of assumptions, which we call sparseness assumptions. It is proved that, under general conditions, the proposed family of statistics based on Rao's divergence is asymptotically normal when the sample size n and the number of cells Mn tend to infinity so that n/Mn→ v > 0. This result is extended to contiguous alternatives, and subsequently it is possible to find the asymptotically most efficient member of the family.


Item Type:Article
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This work was supported by grant BMF 2000-0800.

Uncontrolled Keywords:Goodness-of-fit test;R/-divergence; Sparseness assumptions; Power function; Efficiency
Subjects:Sciences > Mathematics > Mathematical statistics
ID Code:17603
Deposited On:10 Jan 2013 09:36
Last Modified:12 Dec 2018 18:17

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