Abstract
In this paper we motivate a random coefficient autoregressive process of order 1 for describing reliability growth or decay. We introduce several ramifications of this process, some of which reduce it to a Kalman Filter model. We illustrate the usefulness of our approach by applying these processes to some real life data on software failures. Finally, we make a pairwise comparison of the models in terms of the ratio of likelihoods of their predictive distributions, and identify the “best” model. Copyright © 1985 by The Institute of Electrical and Electronics Engineers, Inc.
| Original language | English |
|---|---|
| Pages (from-to) | 1456-1464 |
| Journal | IEEE Transactions on Software Engineering |
| Volume | SE-11 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - Dec 1985 |
| Externally published | Yes |
Research Keywords
- Dynamic linear and nonlinear models
- Kalman Filtering
- likelihood ratios
- predictive distributions
- prequential analysis
- random coefficient autoregressive processes
- reliability growth
- software reliability
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