Methods for estimating a conditional distribution function
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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|Item Type:||Journal Article|
|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:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)|
|Divisions:||Current > QUT Faculties and Divisions > QUT Business School|
|Copyright Owner:||Copyright 1999 American Statistical Association|
|Copyright Statement:||Reproduced in accordance with the copyright policy of the publisher.|
|Deposited On:||07 Feb 2007|
|Last Modified:||05 Jan 2011 13:29|
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