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Determining Process Death Based on Censored Activity Data |
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| Authors: | Nicholas Evangelopoulos Anna Sidorova Stergios Fotopoulos Indushobha Chengalur-Smith |
| Affiliation: | 1. Department of Information Technology and Decision Sciences , University of North Texas , Denton, Texas, USA evangeln@unt.edu;3. Department of Information Technology and Decision Sciences , University of North Texas , Denton, Texas, USA;4. Department of Management and Operations , Washington State University , Pullman, Washington, USA;5. Information Technology Management , School of Business, University of Albany-SUNY , Albany, New York, USA |
| Abstract: | This article addresses the problem of estimating the time of apparent death in a binary stochastic process. We show that, when only censored data are available, a fitted logistic regression model may estimate the time of death incorrectly. We improve this estimation by utilizing discrete-event simulation to produce simulated complete time series data. The proposed methodology may be applied to situations where time of death cannot be formally determined and has to be estimated based on prolonged inactivity. As an illustration, we use observed monthly activity patterns from 300 real Open Source Software development projects sampled from Sourceforge.net. |
| Keywords: | Discrete event simulations Logistic regression Open source software Process activity Survival analysis |