@book{6b472c3ed8384be08f83be420a47a093,

title = "The least squares method in heteroscedastic censored regression models",

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.",

author = "{Van Keilegom}, I. and M.G. Akritas",

year = "1999",

language = "English",

series = "Memorandum COSOR",

publisher = "Technische Universiteit Eindhoven",

}