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The likelihood ratio test for general mixture models with or without structural parameter

Jean-Marc Azaïs, Élisabeth Gassiat, Cécile Mercadier (2009)

ESAIM: Probability and Statistics

This paper deals with the likelihood ratio test (LRT) for testing hypotheses on the mixing measure in mixture models with or without structural parameter. The main result gives the asymptotic distribution of the LRT statistics under some conditions that are proved to be almost necessary. A detailed solution is given for two testing problems: the test of a single distribution against any mixture, with application to Gaussian, Poisson and binomial distributions; the test of the number of populations...

The multisample version of the Lepage test

František Rublík (2005)

Kybernetika

The two-sample Lepage test, devised for testing equality of the location and scale parameters against the alternative that at least for one of the parameters the equality does not hold, is extended to the general case of k > 1 sampled populations. It is shown that its limiting distribution is the chi-square distribution with 2 ( k - 1 ) degrees of freedom. This k -sample statistic is shown to yield consistent test and a formula for its noncentrality parameter under Pitman alternatives is derived. For some particular...

Variance of periodic measure of bounded set with random position

Jiří Janáček (2006)

Commentationes Mathematicae Universitatis Carolinae

The principal term in the asymptotic expansion of the variance of the periodic measure of a ball in d under uniform random shift is proportional to the ( d + 1 ) st power of the grid scaling factor. This result remains valid for a bounded set in d with sufficiently smooth isotropic covariogram under a uniform random shift and an isotropic rotation, and the asymptotic term is proportional also to the ( d - 1 ) -dimensional measure of the object boundary. The related coefficients are calculated for various periodic...

Wild bootstrap in RCA(1) model

Zuzana Prášková (2003)

Kybernetika

In the paper, a heteroskedastic autoregressive process of the first order is considered where the autoregressive parameter is random and errors are allowed to be non-identically distributed. Wild bootstrap procedure to approximate the distribution of the least-squares estimator of the mean of the random parameter is proposed as an alternative to the approximation based on asymptotic normality, and consistency of this procedure is established.

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