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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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Abstract

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
Official URL: http://www.bernoulli-society.org/index.php/publica...
Additional Information: © 2011 ISI/BS.
Library of Congress subject classification: Q Science > QA Mathematics
Sets: Departments > Economics
Research centres and groups > Suntory and Toyota International Centres for Economics and Related Disciplines (STICERD)
Research centres and groups > Financial Markets Group (FMG)
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 08 Jun 2011 10:59
URL: http://eprints.lse.ac.uk/33809/

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