Abstract Details
Activity Number:
|
415
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Nonparametric Statistics
|
Abstract #313319
|
|
Title:
|
Oracally Efficient Spline Smoothing of Functional Coefficient Regression Models with Simultaneous Confidence Band
|
Author(s):
|
Weixin Cai*+ and Prabir Burman and Joshua Patrick
|
Companies:
|
University of California, Davis and University of California, Davis and University of California, Davis
|
Keywords:
|
Nonlinear time series ;
confidence band ;
oracle efficiency
|
Abstract:
|
This poster examines the confidence band for fitting Functional Coefficient Autoregressive (FCAR) model by implementing spline-backfitting technique. The technique is both computationally expedient for analyzing high dimensional time series data, and theoretically reliable as the estimators are oracally efficient. The method will also provide asymptotic results that allow for confidence bands for the estimates of coefficient functions. The feasibility of both spline-backfitted kernel and spline-backfitted spline approach will be investigated. The new method will be illustrated with stimulation results and "real-world" data examples.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.