TY  - GEN
N2  - 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.
A1  - Polonik, Wolfgang
A1  - Yao, Qiwei
Y1  - 1998///
ID  - heidok21423
AV  - none
CY  - Heidelberg
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/21423/
TI  - Conditional Minimum Volume Predictive Regions For Stochastic Processes
N1  - auch erschienen in: Journal of the American Statistical Association, No. 450. (June 2000), S. 509-519; 
nur Abstract
EP  - 22
ER  -