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Conditional Minimum Volume Predictive Regions For Stochastic Processes

Polonik, Wolfgang ; Yao, Qiwei

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Motivated by interval/region prediction in nonlinear timeseries, we propose a minimum volume predictor (MV-predictor) for astrictly stationary process. The MV-predictor varies with respect tothe current position inthe state space and has the minimum Lebesgue measure amongall regions with the nominal coverage probability.We have established consistency, convergence rates, andasymptotic normality for both coverage probability and Lebesguemeasure of the estimated MV-predictor under the assumption thatthe observations are taken from a strong mixing process.Applications with both real and simulated data sets illustrate theproposed methods.

Item Type: Working paper
Place of Publication: Heidelberg
Date Deposited: 01 Jul 2016 07:06
Date: 1998
Number of Pages: 22
Faculties / Institutes: The Faculty of Mathematics and Computer Science > Department of Applied Mathematics
Subjects: 310 General statistics
510 Mathematics
Schriftenreihe ID: Beiträge zur Statistik > Reports
Additional Information: auch erschienen in: Journal of the American Statistical Association, No. 450. (June 2000), S. 509-519; nur Abstract
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