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Conditional minimum volume predictive regions for stochastic processes

By Wolfgang Polonik and Qiwei Yao

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

Topics: HA Statistics
Publisher: American Statistical Association
Year: 2000
DOI identifier: 10.2307/2669395
OAI identifier: oai:eprints.lse.ac.uk:6311
Provided by: LSE Research Online
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