In linear mixed models the assessing of the significance of all or a subset of the random effects is often of primary interest. Many techniques have been proposed for this purpose but none of them is completely satisfactory. One of the oldest methods for testing randomness is the F-test but it is often overlooked in modern applications due to poor statistical power and non-applicability in some important situations. In this work a two-step procedure is developed for generalizing an F-test and improving its statistical power. In the first step, by comparing two covariance matrices of a least squares statistic, we obtain a ”repeatable” F-type test. In the second step, by changing the projected matrix which defines the least squares statistic we apply the test repeteadly to the same data in order to have a set of correlated statistics analyzed within a multiple testing approach. The resulting test is sufficiently general, easy to compute, with an exact distribution under the null and alternative hypothesis and, perhaps more importantly, with a strong increase of statistical power with respect to the F-test.

An F-type Multiple Testing Approach for Assessing Randomness in Linear Mixed Models / Barnabani Marco. - In: AMERICAN REVIEW OF MATHEMATICS AND STATISTICS. - ISSN 2374-2348. - STAMPA. - Vol. 7(2019), pp. 1-14. [10.15640/arms.v7n2a1]

An F-type Multiple Testing Approach for Assessing Randomness in Linear Mixed Models

Barnabani Marco
2019

Abstract

In linear mixed models the assessing of the significance of all or a subset of the random effects is often of primary interest. Many techniques have been proposed for this purpose but none of them is completely satisfactory. One of the oldest methods for testing randomness is the F-test but it is often overlooked in modern applications due to poor statistical power and non-applicability in some important situations. In this work a two-step procedure is developed for generalizing an F-test and improving its statistical power. In the first step, by comparing two covariance matrices of a least squares statistic, we obtain a ”repeatable” F-type test. In the second step, by changing the projected matrix which defines the least squares statistic we apply the test repeteadly to the same data in order to have a set of correlated statistics analyzed within a multiple testing approach. The resulting test is sufficiently general, easy to compute, with an exact distribution under the null and alternative hypothesis and, perhaps more importantly, with a strong increase of statistical power with respect to the F-test.
Vol. 7
1
14
Barnabani Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/1183208
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