title: Conditional Minimum Volume Predictive Regions For Stochastic Processes
creator: Polonik, Wolfgang
creator: Yao, Qiwei
subject: 310
subject: 310 General statistics
subject: 510
subject: 510 Mathematics
description: 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.
date: 1998
type: Working paper
type: info:eu-repo/semantics/workingPaper
type: NonPeerReviewed
identifier: DOI:10.11588/heidok.00021423
identifier: urn:nbn:de:bsz:16-heidok-214231
identifier:   Polonik, Wolfgang ; Yao, Qiwei  (1998) Conditional Minimum Volume Predictive Regions For Stochastic Processes.  [Working paper]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/21423/
rights: info:eu-repo/semantics/openAccess
rights: Please see front page of the work (Sorry, Dublin Core plugin does not recognise license id)