Abstract
Bayes decision procedures are considered for change point estimation in the simple bilinear segmented model. A discretized normal prior density is employed as the prior distribution for the change point index. Posterior probability functions are developed for this index under a vague prior formulation on the regression parameters. The procedure is applied to an example involving mercury toxicity data.
Original language | English (US) |
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Pages (from-to) | 777-782 |
Number of pages | 6 |
Journal | Biometrical Journal |
Volume | 29 |
Issue number | 7 |
DOIs | |
State | Published - 1987 |
Externally published | Yes |
Keywords
- Bayes analysis
- Nonlinear regression
- Segmented regression
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty