Abstract
A non-Gaussian autoregressive-like model is presented for time series that exhibit occasional large increases in value, termed pulses, and exponential decay between pulses. The model differs from a first-order autoregressive process in its incorporation of feedback between the distribution of the current innovation and the history of the process. Likelihood-based methods of inference for the model are developed, and an application to endocrinological data is given.
Original language | English (US) |
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Pages (from-to) | 354-359 |
Number of pages | 6 |
Journal | Journal of the American Statistical Association |
Volume | 84 |
Issue number | 406 |
DOIs | |
State | Published - Jun 1989 |
Keywords
- Autoregressive process
- Feedback
- Luteinizing hormone
- Mixture distribution
- Non-Gaussian time series
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty