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Nonparametric regression with filtered data

Linton, Oliver, Mammen, Enno, Nielsen, Jens Perch and Van Keilegom, Ingrid (2011) Nonparametric regression with filtered data. Bernoulli, 17 (1). pp. 60-87. ISSN 1350-7265

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Identification Number: 10.3150/10-BEJ260


We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.

Item Type: Article
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Additional Information: © 2011 ISI/BS.
Divisions: Economics
Financial Markets Group
Subjects: Q Science > QA Mathematics
Date Deposited: 08 Jun 2011 10:59
Last Modified: 20 Jul 2021 00:25

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