A GIC rule for assessing data transformation in regression

DOI10.1016/j.spl.2004.01.019zbMath1058.62056OpenAlexW2057226661MaRDI QIDQ1770069

Song-Gui Wang, Zhong-Zhen Jia, Heung Wong, Wai-Cheung Ip

Publication date: 7 April 2005

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

Full work available at URL: https://doi.org/10.1016/j.spl.2004.01.019

zbMATH Keywords

Box-Cox transformationConstructed variableGeneralized information criterionPenalty parameter

Mathematics Subject Classification ID

Uses Software

• MINITAB

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Asymptotic properties of criteria for selection of variables in multiple regression
• Estimating the dimension of a model
• Modified AIC rule for model selection in combination with prior estimated noise models
• Model selection under nonstationarity: Autoregressive models and stochastic linear regression models
• Statistical predictor identification
• Transformations and Influential Cases in Regression
• The Analysis of Transformed Data
• Diagnostic Tests for Transformations
• Variable Selection in Nonparametric Regression with Categorical Covariates
• The Relationship between Variable Selection and Data Agumentation and a Method for Prediction
• Linear Model Selection by Cross-Validation
• A note on the selection of data transformations
• Some Comments on C P
• Asymptotic theory for Box-Cox transformations in linear models.

This page was built for publication: A GIC rule for assessing data transformation in regression