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Conditional minimum volume predictive regions for stochastic processes

Polonik, Wolfgang and Yao, Qiwei (2000) Conditional minimum volume predictive regions for stochastic processes. Journal of the American Statistical Association, 95 (450). pp. 509-519. ISSN 0162-1459

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Abstract

Motivated by interval/region prediction in nonlinear time series, we propose a minimum volume predictor (MV-predictor) for a strictly stationary process. The MV-predictor varies with respect to the current position in the state space and has the minimum Lebesgue measure among all regions with the nominal coverage probability. We have established consistency, convergence rates, and asymptotic normality for both coverage probability and Lebesgue measure of the estimated MV-predictor under the assumption that the observations are taken from a strong mixing process. Applications with both real and simulated data sets illustrate the proposed methods.

Item Type: Article
Official URL: http://www.amstat.org/publications/jasa.cfm
Additional Information: © 2000 American Statistical Association
Library of Congress subject classification: H Social Sciences > HA Statistics
Sets: Collections > Economists Online
Departments > Statistics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Funders: Engineering and Physical Sciences Research Council
Date Deposited: 02 Jul 2008 10:07
URL: http://eprints.lse.ac.uk/6311/

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