Variable selection and transformation in linear regression models

DOI10.1016/j.spl.2004.12.018zbMath1075.62056OpenAlexW2008823812MaRDI QIDQ1779679

In-Kwon Yeo

Publication date: 1 June 2005

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2004.12.018

zbMATH Keywords

Kullback-Leibler informationMonte Carlo estimationParametric transformationBootstrap calibrationCox statistic

Mathematics Subject Classification ID

Linear regression; mixed models (62J05)

Related Items (1)

Oracle estimation of parametric transformation models

Cites Work

• An Alternative Family of Transformations
• Regularity conditions for Cox's test of non-nested hypotheses
• A method for simultaneous variable selection and outlier identification in linear regression
• A simulation approach to the problem of computing Cox's statistic for testing nonnested models
• A new family of power transformations to improve normality or symmetry
• The Encompassing Principle and its Application to Testing Non-Nested Hypotheses
• A New Method for Testing Separate Families of Hypotheses
• Calibrating Confidence Coefficients
• The Large-Sample Behavior of Transformations to Normality
• A test of separate families of distributions based on the empirical moment generating function
• The underlying structure of nonnested hypothesis tests
• The bootstrap and Edgeworth expansion
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