Nonparametric Regression in Natural Exponential Families

Loading...
Thumbnail Image
Penn collection
Statistics Papers
Degree type
Discipline
Subject
adaptivity
asymptotic equivalence
exponential family
James-Stein estimator
Gaussian nonparametric regression
quantile coupling
wavelets
Mathematics
Other Physical Sciences and Mathematics
Statistics and Probability
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Cai, T. Tony
Zhou, Harrison H
Contributor
Abstract

Theory and methodology for nonparametric regression have been particularly well developed in the case of additive homoscedastic Gaussian noise. Inspired by asymptotic equivalence theory, there have been ongoing efforts in recent years to construct explicit procedures that turn other function estimation problems into a standard nonparametric regression with Gaussian noise. Then in principle any good Gaussian nonparametric regression method can be used to solve those more complicated nonparametric models. In particular, Brown, Cai and Zhou [3] considered nonparametric regression in natural exponential families with a quadratic variance function. In this paper we extend the scope of Brown, Cai and Zhou [3] to general natural exponential families by introducing a new explicit procedure that is based on the variance stabilizing transformation. The new approach significantly reduces the bias of the inverse transformation and as a consequence it enables the method to be applicable to a wider class of exponential families. Combining this procedure with a wavelet block thresholding estimator for Gaussian nonparametric regression, we show that the resulting estimator enjoys a high degree of adaptivity and spatial adaptivity with near-optimal asymptotic performance over a broad range of Besov spaces.

Advisor
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Publication date
2010-01-01
Journal title
Institute of Mathematical Statistics Collections
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Recommended citation
Collection