A predictive leverage statistic for quantile regression with measurement errors

Edmore Ranganai, Saralees Nadarajah*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Application of quantile regression models with measurement errors in predictors is becoming increasingly popular. High leverage points in predictors can have substantial impacts on these models. Here, we propose a predictive leverage statistic for these models, assuming that the measurement errors follow a multivariate normal distribution, and derive its exact distribution. We compare its performance versus known predictive leverage statistics using simulation and a real dataset. The proposed statistic is shown to have desirable features. It is also the first predictive leverage statistic having its distribution derived in a closed form.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalCommunications in Statistics: Simulation and Computation
Early online date28 Jul 2016
DOIs
Publication statusPublished - 2016

Keywords

  • Elemental predictive leverage
  • Elemental regression
  • Elemental regression weight
  • Elemental set
  • Multiple case
  • Quantile regression

Fingerprint

Dive into the research topics of 'A predictive leverage statistic for quantile regression with measurement errors'. Together they form a unique fingerprint.

Cite this