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Activity Number:
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426
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #300487 |
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Title:
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Censored Regression by the Method of Average Derivatives
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Author(s):
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Xuewen Lu*+ and Murray D. Burke
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Companies:
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University of Calgary and University of Calgary
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Address:
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Dept. of Mathematics and Statistics, Calgary, AB, T2N 1N4, Canada
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Keywords:
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asymptotic normality ; average derivative ; nonparametric regression ; censoring ; data transformation ; local polynomial
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Abstract:
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This paper proposes a technique [termed censored average derivative estimation (CADE)] for studying estimation of the unknown regression function in nonparametric regression models with randomly censored samples.The CADE procedure involves three stages: first, transform the censored data into synthetic data or pseudo-responses using the IPCW (Inverse Probability Censoring Weighted) technique; secondly, estimate the average derivatives of the regression function; and finally, approximate the unknown regression function by an estimator of univariate regression using techniques for one-dimensional nonparametric censored regression. The CADE provides an easily implemented methodology for modeling the association between the response and a set predictor variables when data are randomly censored. It also provides a technique for "dimension reduction" in nonparametric censored regression models. The average derivative estimator is shown to be root-N consistent and asymptotically normal. The estimator of the unknown regression function is a local linear kernel regression estimator and is shown to converge at the optimal one-dimensional nonparametric rate. A Monte Carlo study is presented.
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