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Abstract Details
Activity Number:
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248
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #302621 |
Title:
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Forecasting Time-to-Event Durations in Multi-Step Processes Using Nonparametric Dynamic Survival Analysis
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Author(s):
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Nan Shao*+ and Alejandro Veen
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Companies:
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IBM Research and IBM Research
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Address:
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, , ,
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Keywords:
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time-to-event data ;
Kaplan-Meier estimate ;
time series ;
forecast
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Abstract:
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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.
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