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