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Nonparametric estimation in heteroskedastic regression

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  • Akritas, Michael G.

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.

Suggested Citation

  • Akritas, Michael G., 1996. "Nonparametric estimation in heteroskedastic regression," Statistics & Probability Letters, Elsevier, vol. 28(1), pages 23-31, June.
  • Handle: RePEc:eee:stapro:v:28:y:1996:i:1:p:23-31
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