Post-\(J\) test inference in non-nested linear regression models

DOI10.1007/s11425-014-4935-7zbMath1327.62401OpenAlexW2005528748MaRDI QIDQ889811

Alan Wan, Yanqin Fan, Guo Hua Zou, Xinjie Chen

Publication date: 9 November 2015

Published in: Science China. Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11425-014-4935-7

zbMATH Keywords

Wald statisticnon-nested linear regressionpost-\(J\) test

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Parametric hypothesis testing (62F03)

Related Items (1)

Least squares model averaging for two non-nested linear models

Cites Work

• Testing linear hypothesis on regression coefficients after a pre-test for disturbance variance
• Bootstrapping J-type tests for non-nested regression models
• Tests of non-nested regression models. Small sample adjustments and Monte Carlo evidence
• On exact and asymptotic tests of non-nested models
• On the impact of the tests for serial correlation upon the test of significance for the regression coefficient
• The distributions of the \(J\) and Cox non-nested tests in regression models with weakly correlated regressors
• The significance of testing empirical non-nested models
• On estimating the common mean in two normal distributions after a preliminary test for equality of variances
• Comparison of Local Power of Alternative Tests of Non-Nested Regression Models
• Several Tests for Model Specification in the Presence of Alternative Hypotheses
• NONNESTED LINEAR MODEL SELECTION REVISITED

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