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Projected likelihood contrasts for testing homogeneity in finite mixture models with nuisance parameters

Debapriya Sengupta and Rahul Mazumder

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Abstract

This paper develops a test for homogeneity in finite mixture models where the mixing proportions are known a priori (taken to be 0.5) and a common nuisance parameter is present. Statistical tests based on the notion of Projected Likelihood Contrasts (PLC) are considered. The PLC is a slight modification of the usual likelihood ratio statistic or the Wilk’s Λ and is similar in spirit to the Rao’s score test. Theoretical investigations have been carried out to understand the large sample statistical properties of these tests. Simulation studies have been carried out to understand the behavior of the null distribution of the PLC statistic in the case of Gaussian mixtures with unknown means (common variance as nuisance parameter) and unknown variances (common mean as nuisance parameter). The results are in conformity with the theoretical results obtained. Power functions of these tests have been evaluated based on simulations from Gaussian mixtures.

Chapter information

Source
N. Balakrishnan, Edsel A. Peña and Mervyn J. Silvapulle, eds., Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008), 272-281

Dates
First available in Project Euclid: 1 April 2008

Permanent link to this document
https://projecteuclid.org/euclid.imsc/1207058279

Digital Object Identifier
doi:10.1214/193940307000000194

Mathematical Reviews number (MathSciNet)
MR2462211

Subjects
Primary: 62G08: Nonparametric regression 60G35: Signal detection and filtering [See also 62M20, 93E10, 93E11, 94Axx]
Secondary: 60J55: Local time and additive functionals

Keywords
Gaussian mixture models projected likelihood contrast

Rights
Copyright © 2008, Institute of Mathematical Statistics

Citation

Sengupta, Debapriya; Mazumder, Rahul. Projected likelihood contrasts for testing homogeneity in finite mixture models with nuisance parameters. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen, 272--281, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2008. doi:10.1214/193940307000000194. https://projecteuclid.org/euclid.imsc/1207058279


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References

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