Abstract Details
Activity Number:
|
544
|
Type:
|
Topic Contributed
|
Date/Time:
|
Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
|
Sponsor:
|
IMS
|
Abstract #313425
|
View Presentation
|
Title:
|
Generalized P-Value for Two-Sample Functional Data Comparison
|
Author(s):
|
Yixuan Qiu*+ and Lingsong Zhang
|
Companies:
|
Purdue University and Purdue Univeristy
|
Keywords:
|
generalized p-value ;
functional data ;
mean comparison ;
high-dimensional testing ;
Type I error
|
Abstract:
|
Hypothesis testing lies in the central part of statistical inference, while it is still challenging in functional data analysis because of the infinite dimension nature. In this talk we transform the test problem of functional means into a high-dimensional two sample comparison problem. By incorporating the concept of generalized p-value, we are able to derive a parameter-free test variable. Simulation studies show that the proposed testing approach has a Type I error not greater than the given significance level. Comparisons between our method and other existing methods are reported in terms of size and power. We also apply the proposed method to real data, indicating the effectiveness of the test variable to detect difference in functional curves.
|
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.