Displaying similar documents to “Generalized F tests and selective generalized F tests for orthogonal and associated mixed models”

Selective generalized F tests

C. Nunes, J. T. Mexia (2004)

Discussiones Mathematicae Probability and Statistics

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Generalized F tests were introduced by Michalski and Zmyślony (1996) for variance components and later (1999) for linear functions of parameters in mixed linear models. We now use generalized polar coordinates to obtain, for the second case, tests that are more powerful for selected families of alternatives.

On testing variance components in unbalanced mixed linear model

Lýdia Širková, Viktor Witkovský (2001)

Applications of Mathematics

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The paper presents some approximate and exact tests for testing variance components in general unbalanced mixed linear model. It extends the results presented by Seifert (1992) with emphasis on the computational aspects of the problem.

F and selective F tests with balanced cross-nesting and associated models

Célia Nunes, Iola Pinto, João Tiago Mexia (2006)

Discussiones Mathematicae Probability and Statistics

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F tests and selective F tests for fixed effects part of balanced models with cross-nesting are derived. The effects of perturbations in the numerator and denominator of the F statistics are considered.

Tests of independence of normal random variables with known and unknown variance ratio

Edward Gąsiorek, Andrzej Michalski, Roman Zmyślony (2000)

Discussiones Mathematicae Probability and Statistics

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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...

F-tests for generalized linear hypotheses in subnormal models

Joao Tiago Mexia, Gerberto Carvalho Dias (2001)

Discussiones Mathematicae Probability and Statistics

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When the measurement errors may be assumed to be normal and independent from what is measured a subnormal model may be used. We define a linear and generalized linear hypotheses for these models, and derive F-tests for them. These tests are shown to be UMP for linear hypotheses as well as strictly unbiased and strongly consistent for these hypotheses. It is also shown that the F-tests are invariant for regular transformations, possess structural stability and are almost strongly consistent...

A comparison of cointegration tests

Petr Mariel (1996)

Applications of Mathematics

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In this paper some of the cointegration tests applied to a single equation are compared. Many of the existent cointegration tests are simply extensions of the unit root tests applied to the residuals of the cointegrating regression and the habitual H 0 is no cointegration. However, some non residual-based tests and some tests of the opposite null hypothesis have recently appeared in literature. Monte Carlo simulations have been used for the power comparison of the nine selected tests (...

On a robust significance test for the Cox regression model

Tadeusz Bednarski, Filip Borowicz (2006)

Discussiones Mathematicae Probability and Statistics

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A robust significance testing method for the Cox regression model, based on a modified Wald test statistic, is discussed. Using Monte Carlo experiments the asymptotic behavior of the modified robust versions of the Wald statistic is compared with the standard significance test for the Cox model based on the log likelihood ratio test statistic.

Unit root test under innovation outlier contamination small sample case

Lynda Atil, Hocine Fellag, Karima Nouali (2006)

Discussiones Mathematicae Probability and Statistics

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The two sided unit root test of a first-order autoregressive model in the presence of an innovation outlier is considered. In this paper, we present three tests; two are usual and one is new. We give formulas computing the size and the power of the three tests when an innovation outlier (IO) occurs at a specified time, say k. Using a comparative study, we show that the new statistic performs better under contamination. A Small sample case is considered only.

Sign and Wilcoxon tests for quadratic versus cubic regression.

A. P. Gore, K. S. Madhava Rao (1984)

Trabajos de Estadística e Investigación Operativa

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In this paper sign and Wilcoxon tests for testing the null hypothesis of quadratic regression versus the alternative, cubic regression are proposed. It is shown that in the case of a simple design consisting of multiple Y observations at each of the four levels of x, the proposed tests perform reasonably well as compared to their parametric competitors, while in the case of a general design consisting of a large number of levels of x, the loss in Pitman efficiency is considerable. However...