The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
|
Activity Number:
|
249
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 1, 2011 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Statistics and the Environment
|
| Abstract - #300901 |
|
Title:
|
Robust Confidence Interval for Zero-Heavy Distribution
|
|
Author(s):
|
Mathew Anthony Cantos Rosales*+ and Magdalena Niewiadomska-Bugaj
|
|
Companies:
|
COMSYS and Western Michigan University
|
|
Address:
|
5220 Lovers Lane STE 200, Portage, MI, 49002,
|
|
Keywords:
|
zero-heavy ;
lognormal distribution ;
Monte-Carlo ;
robust estimator ;
delta distribution ;
confidence interval
|
|
Abstract:
|
Zero-heavy data are very common in many disciplines like insurance, medical research, life sciences, marine sciences and engineering. In real life (e.g., in marine sciences), data that are said to be from lognormal distribution were often better fit by other skewed distributions (Myers and Pepin, 1990). This is in addition to the fact that goodness-of-fit test could not reliably detect departure from lognormality of the positive observation when sample size is small. In this paper, robustness of the interval estimators for the mean of lognormal distribution with a positive mass at zero was investigated and a new method was proposed. A comprehensive Monte-Carlo simulation study was performed and revealed that the proposed method outperforms other methods in terms of coverage probability and interval width when the data depart from the assumed model or are contaminated by extremely large values.
|
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 2011 program
|
2011 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.