Tests of fit for the logarithmic distribution.
Tests of independence of normal random variables with known and unknown variance ratio
In the paper, a new approach to construction test for independenceof two-dimensional normally distributed random vectors is given under the assumption that the ratio of the variances is known. This test is uniformly better than the t-Student test. A comparison of the power of these two tests is given. A behaviour of this test forsome ε-contamination of the original model is also shown. In the general case when the variance ratio is unknown, an adaptive test is presented. The equivalence between...
Tests of Significance for the Average Square Deviation from Target in a Finite Lot.
Tests simultanés de permutation des rangs pour bruit-blanc multivarié
The Berry-Esseen theorem for rank statistics
The distribution of the estimate of entropy and its applications
The infinitesimal robustness of tests against dependence
The Large-Sample Power of the x Test fox Multidimensional Contingency Tables.
The likelihood ratio test for general mixture models with or without structural parameter
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 Minimum Distance Method of Testing.
The most significant interaction in a contingency table
The multisample version of the Lepage test
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 sampled populations. It is shown that its limiting distribution is the chi-square distribution with degrees of freedom. This -sample statistic is shown to yield consistent test and a formula for its noncentrality parameter under Pitman alternatives is derived. For some particular...
The robustness against dependence of nonparametric tests for the two-sample location problem
Nonparametric tests for the two-sample location problem are investigated. It is shown that the supremum of the size of any test can be arbitrarily close to 1. None of these tests is most robust against dependence.
Toward the best constant factor for the Rademacher-Gaussian tail comparison
It is proved that the best constant factor in the Rademacher-Gaussian tail comparison is between two explicitly defined absolute constants c1 and c2 such that c2≈1.01 c1. A discussion of relative merits of this result versus limit theorems is given.
Transformations to symmetry based on the probability weighted characteristic function
We suggest a nonparametric version of the probability weighted empirical characteristic function (PWECF) introduced by Meintanis et al. [10] and use this PWECF in order to estimate the parameters of arbitrary transformations to symmetry. The almost sure consistency of the resulting estimators is shown. Finite-sample results for i.i.d. data are presented and are subsequently extended to the regression setting. A real data illustration is also included.
Trimmed polynomial regression
Two families of normality tests based on Polya characterization, and their efficiency.
Two sample nonparametric procedures based on sample coverages for uncensored data.
Two-sample rank tests based on exceeding observations
Simple rank statistics are used to test that two samples come from the same distribution. Šidák’s -test (Apl. Mat. 22 (1977), 166–175) is based on the number of observations from one sample that exceed all observations from the other sample. A similar test statistic is defined in Ann. Inst. Stat. Math. 52 (1970), 255–266. We study asymptotic behavior of the moments of both statistics.
Two-Sample Rank Tests with Truncated Populations.