Inferential models for linear regression

Zuoyi Zhang, Huiping Xu, Ryan Martin, Chuanhai Liu

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Linear regression is arguably one of the most widely used statistical methods. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs) in the linear regression context. In general, the IM framework is able to produce meaningful probabilistic summaries of the statistical evidence for and against assertions about the unknown parameter of interest, and these summaries are shown to be properly calibrated in a frequentist sense. Here we demonstrate by example that the IM framework is promising for linear regression analysis---including model checking, variable selection, and prediction---and for uncertain inference in general.

Original languageEnglish (US)
Pages (from-to)413-432
Number of pages20
JournalPakistan Journal of Statistics and Operation Research
Volume7
Issue number2 SPECIAL ISSUE
DOIs
StatePublished - Oct 2011

Keywords

  • Auxiliary variable
  • Credibility
  • Prediction
  • Predictive random set
  • Variable selection

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Statistics, Probability and Uncertainty
  • Modeling and Simulation
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Inferential models for linear regression'. Together they form a unique fingerprint.

  • Cite this