Abstract Details
Activity Number:
|
607
|
Type:
|
Contributed
|
Date/Time:
|
Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Nonparametric Statistics
|
Abstract #316402
|
|
Title:
|
Improving Efficiency in Structural Equation Modeling by an Easy Empirical Likelihood Approach
|
Author(s):
|
Hanxiang Peng* and Shan Wang
|
Companies:
|
Indiana University Purdue University Indianapolis and Indiana University Purdue University Indianapolis
|
Keywords:
|
discrepancy function ;
empirical likelihood ;
efficiency ;
structural equation models ;
maximum empirical likelihood estimates
|
Abstract:
|
In this talk, we present an easy empirical likelihood approach to improving efficiency of estimation in structural equation models. We give the asymptotic results and discuss the efficiency gain of the EL-based approach over the usual one. We explain how numerical computation can be obtained by using existing softwares. We illustrate the implementation by simulations and real data applications.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2015 program
|
For program information, contact the JSM Registration Department or phone (888) 231-3473.
For Professional Development information, 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.
2015 JSM Online Program Home
ASA Meetings Department
732 North Washington Street, Alexandria, VA 22314
(703) 684-1221 • meetings@amstat.org
Copyright © American Statistical Association.