Computational Treatment of the Error Distribution in Nonparametric Regression with Right-Censored and Selection-Biased Data

Géraldine Laurent () and Cédric Heuchenne ()
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Géraldine Laurent: QuantOM, HEC-Management School of University of Liège
Cédric Heuchenne: QuantOM, HEC-Management School of University of Liège

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 509-516 from Springer

Abstract: Abstract Consider the regression model Y = m(X) + φ(X)ε, where m(X) = E[Y ∣X] and φ2(X) = V ar[Y ∣X] are unknown smooth functions and the error ε (with unknown distribution) is independent of X. The pair (X, Y) is subject to parametric selection bias and the response to right censoring. We construct a new estimator for the cumulative distribution function of the error ε, and develop a bootstrap technique to select the smoothing parameter involved in the procedure. The estimator is studied via extended simulations and applied to real unemployment data.

Keywords: nonparametric regression; selection bias; right censoring; bootstrap; bandwidth selection (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_51

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DOI: 10.1007/978-3-7908-2604-3_51

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