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:
|
504
|
Type:
|
Contributed
|
Date/Time:
|
Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Nonparametric Statistics
|
Abstract - #306838 |
Title:
|
Robust Compound Regression: A New Approach for Robust Estimation of Errors-in-Variables Models
|
Author(s):
|
Hao Han*+ and Yeming Ma and Xiangmin Jiao and Ling Leng and Zhengrong Liang and Wei Zhu
|
Companies:
|
SUNY at Stony Brook and National Institutes of Health and SUNY at Stony Brook and SUNY at Stony Brook and SUNY at Stony Brook and SUNY at Stony Brook
|
Address:
|
, Stony Brook, NY, , United States
|
Keywords:
|
Errors-in-variables model ;
robust regression ;
nonparametric regression ;
least sine squares ;
robust compound regression ;
regression efficiency
|
Abstract:
|
The errors-in-variables (EIV) regression model, being more realistic by accounting for measurement errors in both the dependent and the independent variables, is widely adopted in applied sciences. The traditional EIV model estimators, however, can be highly biased by outliers and other departures from the underlying assumptions.
In this paper, we develop a novel nonparametric regression approach - the robust compound regression (RCR) analysis method for the robust estimation of EIV models. We first introduce a robust and efficient estimator called least sine squares (LSS). Taking full advantage of both the new LSS method and the compound regression analysis method developed in our own group, we subsequently propose the RCR approach as a generalization of those two, which provides a robust counterpart of the entire class of the maximum likelihood estimation (MLE) solutions of the EIV model, in a 1-1 mapping. Technically, our approach gives users the flexibility to select from a class of RCR estimates the optimal one with a predefined regression efficiency criterion satisfied. Simulation studies and real-life examples are provided to illustrate the effectiveness of the RCR approach.
|
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.