Kernel deconvolution estimation for random fields

Details Kernel deconvolution estimation for random fields Kernel deconvolution estimation for random fields

2012-01-02

arxiv.org/abs/1201.0470v1

Mathematics

Statistics Theory

28 pages. arXiv admin note: text overlap with arXiv:1109.2694

Scientific paper

In this work, we establish the asymptotic normality of the deconvolution kernel density estimator in the context of strongly mixing random fields. Only minimal conditions on the bandwidth parameter are required and a simple criterion on the strong mixing coefficients is provided. Our approach is based on the Lindeberg's method rather than on Bernstein's technique and coupling arguments widely used in previous works on nonparametric estimation for spatial processes. We deal also with nonmixing random fields which can be written as a (nonlinear) functional of i.i.d. random fields by considering the physical dependence measure coefficients introduced by Wu (2005).

Affiliated with

Also associated with

No associations

Rating

Kernel deconvolution estimation for random fields 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 Kernel deconvolution estimation for random fields, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Kernel deconvolution estimation for random fields will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-55545