Empirical Likelihood Confidence Intervals for Nonparametric Functional Data Analysis

Details Empirical Likelihood Confidence Intervals for Nonparametric Functional Data Analysis Empirical Likelihood Confidence Intervals for Nonparametric Functional Data Analysis

2009-04-06

arxiv.org/abs/0904.0843v1

Statistics

Methodology

Scientific paper

We consider the problem of constructing confidence intervals for nonparametric functional data analysis using empirical likelihood. In this doubly infinite-dimensional context, we demonstrate the Wilks's phenomenon and propose a bias-corrected construction that requires neither undersmoothing nor direct bias estimation. We also extend our results to partially linear regression involving functional data. Our numerical results demonstrated the improved performance of empirical likelihood over approximation based on asymptotic normality.

Affiliated with

Also associated with

No associations

Rating

Empirical Likelihood Confidence Intervals for Nonparametric Functional Data Analysis 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 Empirical Likelihood Confidence Intervals for Nonparametric Functional Data Analysis, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Empirical Likelihood Confidence Intervals for Nonparametric Functional Data Analysis will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-132829