Conditional Akaike information under covariate shift with application to small area estimation

Yuki Kawakubo, Shonosuke Sugasawa, Tatsuya Kubokawa

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In this study, we consider the problem of selecting explanatory variables of fixed effects in linear mixed models under covariate shift, which is when the values of covariates in the model for prediction differ from those in the model for observed data. We construct a variable selection criterion based on the conditional Akaike information introduced by Vaida & Blanchard (2005). We focus especially on covariate shift in small area estimation and demonstrate the usefulness of the proposed criterion. In addition, numerical performance is investigated through simulations, one of which is a design-based simulation using a real dataset of land prices.

Original languageEnglish
Pages (from-to)316-335
Number of pages20
JournalCanadian Journal of Statistics
Volume46
Issue number2
DOIs
Publication statusPublished - 2018 Jun
Externally publishedYes

Keywords

  • Akaike information criterion
  • MSC 2010: Primary 62J05
  • conditional AIC
  • covariate shift
  • linear mixed model
  • secondary 62P25
  • small area estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Conditional Akaike information under covariate shift with application to small area estimation'. Together they form a unique fingerprint.

Cite this