eprintid: 21423
rev_number: 21
eprint_status: archive
userid: 2326
dir: disk0/00/02/14/23
datestamp: 2016-07-01 07:06:27
lastmod: 2016-10-21 09:18:22
status_changed: 2016-09-07 13:47:13
type: workingPaper
metadata_visibility: show
creators_name: Polonik, Wolfgang
creators_name: Yao, Qiwei
title: Conditional Minimum Volume Predictive Regions For Stochastic Processes
subjects: 310
subjects: 510
divisions: 110400
note: auch erschienen in: Journal of the American Statistical Association, No. 450. (June 2000), S. 509-519; 
nur Abstract
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.
date: 1998
id_scheme: DOI
id_number: 10.11588/heidok.00021423
schriftenreihe_cluster_id: sr-10b
schriftenreihe_order: 15
ppn_swb: 478580363
own_urn: urn:nbn:de:bsz:16-heidok-214231
bibsort: POLONIKWOLCONDITIONA1998
full_text_status: none
place_of_pub: Heidelberg
pages: 22
citation:   Polonik, Wolfgang ; Yao, Qiwei  (1998) Conditional Minimum Volume Predictive Regions For Stochastic Processes.  [Working paper]