Empirical likelihood confidence intervals for nonparametric functional data analysis

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 Wilk's phenomenon and propose a bias-corrected construction that requires neither undersmoothing no...

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Main Author: Lian, Heng
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2013
Online Access:https://hdl.handle.net/10356/99370
http://hdl.handle.net/10220/17246
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-993702020-03-07T12:37:17Z Empirical likelihood confidence intervals for nonparametric functional data analysis Lian, Heng School of Physical and Mathematical Sciences 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 Wilk'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 models involving functional data. Our numerical results demonstrate improved performance of the empirical likelihood methods over normal approximation-based methods. 2013-11-05T04:36:33Z 2019-12-06T20:06:31Z 2013-11-05T04:36:33Z 2019-12-06T20:06:31Z 2012 2012 Journal Article Lian, H. (2012). Empirical likelihood confidence intervals for nonparametric functional data analysis. Journal of Statistical Planning and Inference, 142(7), 1669-1677. 0378-3758 https://hdl.handle.net/10356/99370 http://hdl.handle.net/10220/17246 10.1016/j.jspi.2012.02.008 en Journal of statistical planning and inference
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
description 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 Wilk'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 models involving functional data. Our numerical results demonstrate improved performance of the empirical likelihood methods over normal approximation-based methods.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Lian, Heng
format Article
author Lian, Heng
spellingShingle Lian, Heng
Empirical likelihood confidence intervals for nonparametric functional data analysis
author_sort Lian, Heng
title Empirical likelihood confidence intervals for nonparametric functional data analysis
title_short Empirical likelihood confidence intervals for nonparametric functional data analysis
title_full Empirical likelihood confidence intervals for nonparametric functional data analysis
title_fullStr Empirical likelihood confidence intervals for nonparametric functional data analysis
title_full_unstemmed Empirical likelihood confidence intervals for nonparametric functional data analysis
title_sort empirical likelihood confidence intervals for nonparametric functional data analysis
publishDate 2013
url https://hdl.handle.net/10356/99370
http://hdl.handle.net/10220/17246
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