Nonparametric estimation in heteroskedastic regression

Michael G. Akritas

Research output: Contribution to journalArticlepeer-review

Abstract

We consider the problem of making inferences about the parameters in a heteroskedastic regression model using the ranks of weighted observations. The model assumes symmetric error distribution and a parametric model for the error variance. It is shown that there is no loss in asymptotic efficiency due to estimating the unknown weights. This extends the theory of rank estimation in the heteroskedastic linear model.

Original languageEnglish (US)
Pages (from-to)23-31
Number of pages9
JournalStatistics and Probability Letters
Volume28
Issue number1
DOIs
StatePublished - Jun 1 1996

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Nonparametric estimation in heteroskedastic regression'. Together they form a unique fingerprint.

Cite this