The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Online Program Home
Abstract Details
|
Activity Number:
|
642
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Nonparametric Statistics
|
| Abstract - #306145 |
|
Title:
|
Semiparametric Mixtures of Regressions
|
|
Author(s):
|
David Hunter*+ and Derek Young
|
|
Companies:
|
Penn State University and U.S. Census Bureau
|
|
Address:
|
Department of Statistics, State College, PA, ,
|
|
Keywords:
|
Density Estimation ;
EM Algorithm ;
Finite Mixture Model ;
Identifiability
|
|
Abstract:
|
We present an algorithm for estimating parameters in a mixture-of-regressions model in which the errors are assumed to be independent and identically distributed but no other assumption is made. A sufficient condition for the identifiability of the parameters is stated and a proof is outlined. Several different versions of the algorithm, including one that has a provable ascent property, are introduced. Numerical tests indicate the effectiveness of some of these algorithms.
|
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 2012 program
|
2012 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.