Nonparametric estimation in heteroskedastic regression

Michael G. Akritas

doi:10.1016/0167-7152(95)00077-1

Nonparametric estimation in heteroskedastic regression

Michael G. Akritas

Statistics

Research output: Contribution to journal › Article › peer-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 language	English (US)
Pages (from-to)	23-31
Number of pages	9
Journal	Statistics and Probability Letters
Volume	28
Issue number	1
DOIs	https://doi.org/10.1016/0167-7152(95)00077-1
State	Published - Jun 1 1996

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1016/0167-7152(95)00077-1

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AB - 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.

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Nonparametric estimation in heteroskedastic regression

Abstract

All Science Journal Classification (ASJC) codes

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