Displaying similar documents to “On non-nested regression models”

Testing hypotheses in universal models

Eva Fišerová (2006)

Discussiones Mathematicae Probability and Statistics

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A linear regression model, when a design matrix has not full column rank and a covariance matrix is singular, is considered. The problem of testing hypotheses on mean value parameters is studied. Conditions when a hypothesis can be tested or when need not be tested are given. Explicit forms of test statistics based on residual sums of squares are presented.

Kolmogorov-Smirnov two-sample test based on regression rank scores

Martin Schindler (2008)

Applications of Mathematics

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We derive the two-sample Kolmogorov-Smirnov type test when a nuisance linear regression is present. The test is based on regression rank scores and provides a natural extension of the classical Kolmogorov-Smirnov test. Its asymptotic distributions under the hypothesis and the local alternatives coincide with those of the classical test.

Selection in parametric models via some stepdown procedures

Konrad Furmańczyk (2014)

Applicationes Mathematicae

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The paper considers the problem of consistent variable selection in parametic models with the use of stepdown multiple hypothesis procedures. Our approach completes the results of Bunea et al. [J. Statist. Plann. Inference 136 (2006)]. A simulation study supports the results obtained.

Tests in weakly nonlinear regression model

Lubomír Kubáček, Eva Tesaříková (2005)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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In weakly nonlinear regression model a weakly nonlinear hypothesis can be tested by linear methods if an information on actual values of model parameters is at our disposal and some condition is satisfied. In other words we must know that unknown parameters are with sufficiently high probability in so called linearization region. The aim of the paper is to determine this region.