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:
|
248
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 1, 2011 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Business and Economic Statistics Section
|
| Abstract - #302621 |
|
Title:
|
Forecasting Time-to-Event Durations in Multi-Step Processes Using Nonparametric Dynamic Survival Analysis
|
|
Author(s):
|
Nan Shao*+ and Alejandro Veen
|
|
Companies:
|
IBM Research and IBM Research
|
|
Address:
|
, , ,
|
|
Keywords:
|
time-to-event data ;
Kaplan-Meier estimate ;
time series ;
forecast
|
|
Abstract:
|
Accurate and timely forecasting of event occurrence plays an important role in numerous business applications such processing applications for loans or jobs. When the underlying distributions of the time-to-event durations change over time, the more recent data is more relevant for forecasting. However, a large proportion of recent events are still ongoing, and this causes censoring and thus limits the information. We propose a practical solution to the forecasting problem in such situations which includes a combination of non-parametric estimation and time series forecasting.
|
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.