Polonik, Wolfgang ; Yao, Qiwei
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Abstract
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.
Document type: | Working paper |
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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 > Institut für Mathematik |
DDC-classification: | 310 General statistics 510 Mathematics |
Series: | Beiträge zur Statistik > Reports |
Additional Information: | auch erschienen in: Journal of the American Statistical Association, No. 450. (June 2000), S. 509-519; nur Abstract |