Empirical likelihood confidence intervals for nonparametric functional data analysis
By Heng Lian. 2009
50 citations recorded by the source corpus.
Abstract 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.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/ffc3d8eed0e6bfa2d7500a07d37b4fd574277cec
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In Artificial Intelligence
In Statistics and Probability
Citations in the corpus
- Multivariate analysis of variance for functional data (2017, methodology): “Particular problems considered in functional data analysis include canonical correlation analysis [29,30,33], cluster analysis [27,49], confidence intervals [32], discriminant analysis [4,18,47], linear and nonlinear models [3,15,35], the one-way ANOVAproblem [12,14,20,25,26,50,51], principal compon”