Superefficiency in Nonparametric Function Estimation

Loading...
Thumbnail Image
Penn collection
Statistics Papers
Degree type
Discipline
Subject
superefficiency
nonparametric function estimation
asymptotics
Statistics and Probability
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Brown, Lawrence D
Low, Mark G
Zhao, Linda H
Contributor
Abstract

Fixed parameter asymptotic statements are often used in the context of nonparametric curve estimation problems (e.g., nonparametric density or regression estimation). In this context several forms of superefficiency can occur. In contrast to what can happen in regular parametric problems, here every parameter point (e.g., unknown density or regression function) can be a point of superefficiency. We begin with an example which shows how fixed parameter asymptotic statements have often appeared in the study of adaptive kernel estimators, and how superefficiency can occur in this context. We then carry out a more systematic study of such fixed parameter statements. It is shown in four general settings how the degree of superefficiency attainable depends on the structural assumptions in each case.

Advisor
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Publication date
1997
Journal title
The Annals of Statistics
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Recommended citation
Collection