|
Activity Number:
|
248
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
IMS
|
| Abstract - #308915 |
|
Title:
|
Estimating the Error Distribution Function in Semiparametric Regression
|
|
Author(s):
|
Ursula Mueller-Harknett*+
|
|
Companies:
|
Texas A&M University
|
|
Address:
|
Dept of Statistics, College Station, TX, 77843-3143,
|
|
Keywords:
|
local linear smoother ; i.i.d. representation ; efficiency
|
|
Abstract:
|
In regression models the object of primary statistical interest is the regression function. Estimators of the error distribution function are, however, also of interest, for example for tests about the regression function. There is a large literature on estimating error distribution functions, but it is nearly exclusively concerned with cases in which the regression function is parametric and can be estimated at root-n rate. We consider a partly linear regression model. Here different arguments are needed. We estimate the error distribution function by a empirical distribution function based on residuals. The residuals involve local linear smoothers. We prove a stochastic expansion for the estimator which implies a functional central limit theorem.
|
- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
Back to the full JSM 2007 program |
