Displaying similar documents to “A simple technique for proving unbiasedness of tests and confidence regions”

Parametric test for change in a parameter occurring in the density of one-parameter exponential family

van Huu Nguyen (1980)

Aplikace matematiky

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The problem of testing hypothesis under which the observations are independent, identically distributed against a class of alternatives of regression in a parameter of the one-parameter exponential family is studied. A parametric test for this problem is suggested. The relative efficiency of the parametric test compared to the rank test proposed in the author's preceding paper is also derived.

Unbiased estimators of multivariate discrete distributions and chi-square goodness-of-fit test.

Mikhail S. Nikulin, Vassiliy G. Voinov (1993)

Qüestiió

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We consider the problem of estimation of the value of a real-valued function u(θ), θ = (θ, ..., θ), on the basis of a sample from non-truncated or truncated multivariate Modified Power Series Distributions. Using the general theory of estimation and the results of Patil (1965) and Patel (1978) we give the tables of MVUE's for functions of parameter θ of trinomial, multinomial, negative-multinomial and left-truncated modified power series distributions. We have applied the properties...

A nonparametric test of zero intrapair correlation

Antonín Lukš (1983)

Aplikace matematiky

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The author applies the test criterion of P. Rothety to the statistical analysis of the positive correclation of symmetric pairs of observations. In this particular case he arrives at some new results. His work ends with a general proof of the consistency of Rothery's test.

Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models

Christian Genest, Bruno Rémillard (2008)

Annales de l'I.H.P. Probabilités et statistiques

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In testing that a given distribution belongs to a parameterized family 𝒫 , one is often led to compare a nonparametric estimate of some functional of with an element corresponding to an estimate of . In many cases, the asymptotic distribution of goodness-of-fit statistics derived from the process ( − ) depends on the unknown distribution . It is shown here that if the sequences ...