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Methods for estimating a conditional distribution function

Hall, Peter, Wolff, Rodney C. L., Yao, Qiwei (1999) Methods for estimating a conditional distribution function. Journal of the American Statistical Association, 94 (445). pp. 154-163. ISSN 0162-1459. (doi:10.2307/2669691) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:16830)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL
http://dx.doi.org/10.2307/2669691

Abstract

Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new methods for conditional distribution estimation. The first method is based on locally fitting a logistic model and is in the spirit of recent work on locally parametric techniques in density estimation. It produces distribution estimators that may be of arbitrarily high order but nevertheless always lie between 0 and 1. The second method involves an adjusted form of the Nadaraya-Watson estimator. It preserves the bias and variance properties of a class of second-order estimators introduced by Yu and Jones but has the added advantage of always being a distribution itself. Our methods also have application outside the time series setting: fur example, to quantile estimation for independent data. This problem motivated the work of Yu and Jones.

Item Type: Article
DOI/Identification number: 10.2307/2669691
Uncontrolled keywords: absolutely regular; bandwidth; biased bootstrap; conditional distribution; kernel methods; local linear methods; local logistic methods; Nadaraya-Watson estimator; prediction; quantile estimation; time series analysis; weighted bootstrap
Subjects: H Social Sciences > HA Statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: I.T. Ekpo
Date Deposited: 20 Jun 2009 05:02 UTC
Last Modified: 16 Nov 2021 09:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/16830 (The current URI for this page, for reference purposes)
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