JSM 2013 Home
Online Program Home
My Program

Abstract Details

Activity Number: 41
Type: Contributed
Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #307622
Title: Time-Varying Additive Models for Longitudinal Data
Author(s): Xiaoke Zhang*+ and Byeong U. Park and Jane-Ling Wang
Companies: University of California Davis and Seoul National University and UC Davis
Keywords: Smooth backfitting ; local linear smoothing ; oracle property ; functional data ; longitudinal data
Abstract:

Additive model is an effective dimension reduction approach that also provides flexibility in modeling the relation between a response variable and key covariates. The literature is largely developed to scalar response and vector covariates. In this paper, more complex data is of interest, where both the response and covariates are functions. A functional additive model is proposed together with a new backfitting algorithm to estimate the unknown regression functions, whose components are time-dependent additive functions of the covariates. Such functional data may not be completely observed since measurements may only be collected intermittently at discrete time points. We develop a unified platform and an efficient approach that can cover both dense and sparse functional data and the needed theory for statistical inference. The oracle properties of the proposed estimators of the component functions are also established.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2013 program




2013 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education 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.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.