Displaying similar documents to “The likelihood ratio test for general mixture models with or without structural parameter”

Testing in locally conic models, and application to mixture models

Didier Dacunha-Castelle, Elisabeth Gassiat (2010)

ESAIM: Probability and Statistics

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In this paper, we address the problem of testing hypotheses using maximum likelihood statistics in non identifiable models. We derive the asymptotic distribution under very general assumptions. The key idea is a local reparameterization, depending on the underlying distribution, which is called locally conic. This method enlights how the general model induces the structure of the limiting distribution in terms of dimensionality of some derivative space. We present various applications...

Estimation functions and uniformly most powerful tests for inverse Gaussian distribution

Ion Vladimirescu, Radu Tunaru (2003)

Commentationes Mathematicae Universitatis Carolinae

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The aim of this article is to develop estimation functions by confidence regions for the inverse Gaussian distribution with two parameters and to construct tests for hypotheses testing concerning the parameter λ when the mean parameter μ is known. The tests constructed are uniformly most powerful tests and for testing the point null hypothesis it is also unbiased.