The least squares method in heteroscedastic censored regression models

I. Van Keilegom, M.G. Akritas

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    Abstract

    Consider the heteroscedastic polynomial regression model $ Y = \beta_0 + \beta_1X + ... + \beta_pX^p + \sqrt{Var(Y|X)}\epsilon $, where \epsilon is independent of X, and Y is subject to random censoring. Provided that the censoring on Y is 'light' in some region of X, we construct a least squares estimator for the regression parameters whose asymptotic bias is shown to be as small as desired. The least squares estimator is defined as a functional of the Van Keilegom and Akritas (1999) estimator of the bivariate distribution $P(X \leq x, Y \leq y)$, and its asymptotic normality is obtained.
    Original languageEnglish
    Place of PublicationEindhoven
    PublisherTechnische Universiteit Eindhoven
    Number of pages14
    Publication statusPublished - 1999

    Publication series

    NameMemorandum COSOR
    Volume9918
    ISSN (Print)0926-4493

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