Methods for Estimating a Conditional Distribution Function
作者:
Peter Hall,
RodneyC. L. Wolff,
Qiwei Yao,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1999)
卷期:
Volume 94,
issue 445
页码: 154-163
ISSN:0162-1459
年代: 1999
DOI:10.1080/01621459.1999.10473832
出版商: Taylor & Francis Group
关键词: 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
数据来源: Taylor
摘要:
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; for example, to quantile estimation for independent data. This problem motivated the work of Yu and Jones.
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