Stochastic analysis on Gaussian space applied to drift estimation

Details Stochastic analysis on Gaussian space applied to drift estimation Stochastic analysis on Gaussian space applied to drift estimation

2008-05-14

arxiv.org/abs/0805.2002v1

Mathematics

Statistics Theory

Scientific paper

In this paper we consider the nonparametric functional estimation of the drift of Gaussian processes using Paley-Wiener and Karhunen-Lo\`eve expansions. We construct efficient estimators for the drift of such processes, and prove their minimaxity using Bayes estimators. We also construct superefficient estimators of Stein type for such drifts using the Malliavin integration by parts formula and stochastic analysis on Gaussian space, in which superharmonic functionals of the process paths play a particular role. Our results are illustrated by numerical simulations and extend the construction of James-Stein type estimators for Gaussian processes by Berger and Wolper.

Affiliated with

Also associated with

No associations

Rating

Stochastic analysis on Gaussian space applied to drift estimation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Stochastic analysis on Gaussian space applied to drift estimation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Stochastic analysis on Gaussian space applied to drift estimation will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-55544