Generalized F tests and selective generalized F tests for orthogonal and associated mixed models

• Autorzy: Célia Nunes , Iola Pinto , João Tiago Mexia
• Języki publikacji: EN

Abstrakty

EN

The statistics of generalized F tests are quotients of linear combinations of independent chi-squares. Given a parameter, θ, for which we have a quadratic unbiased estimator, θ̃, the test statistic, for the hypothesis of nullity of that parameter, is the quotient of the positive part by the negative part of such estimator. Using generalized polar coordinates it is possible to obtain selective generalized F tests which are especially powerful for selected families of alternatives. We build both classes of tests for the orthogonal and associated mixed models. The associated models are obtained adding terms to the orthogonal models.

Słowa kluczowe

EN

selective generalized F tests; generalized polar coordinates; associated models

Kategorie tematyczne

• 62H15: Hypothesis testing
• 62H10: Distribution of statistics
• 62J12: Generalized linear models

• Wydawca: Faculty of Mathematics, Computer Science and Econometrics, University of Zielona Góra
• Czasopismo: Discussiones Mathematicae Probability and Statistics
• Rocznik: 2008
• Tom: 28
• Numer: 2
• Strony: 229-246

Daty

wydano

2008

otrzymano

2008-03-18

poprawiono

2008-11-04

Twórcy

autor

Célia Nunes

• Mathematics Department, University of Beira Interior Covilhã, Portugal

autor

Iola Pinto

• Superior Institute of Engineer of Lisbon Scientific Area of Mathematics, Lisboa, Portugal

autor

João Tiago Mexia

• Department of Mathematics, Faculty of Science and Technology New, New University of Lisbon, Monte da Caparica, Portugal

Bibliografia

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• [6] A. Michalski and R. Zmyślony, Testing hypothesis for linear functions of parameters in mixed linear models, Tatra Mountain Mathematical Publications 17 (1999), 103-110.
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• [9] C. Nunes, I. Pinto and J.T. Mexia, F and selective F tests with balanced cross-nesting and associated models, Discussiones Mathematicae, Probability and Statistics 26 (2006), 193-205.
• [10] J.R. Schott, Matrix Analysis for Statistics, Jonh Wiley & Sons, New York 1997.
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Identyfikatory

DOI

10.7151/dmps.1102