The exponential distribution is a fundamental model in parametric survival analysis. In this paper the exponential regression model with a canonical parameterization, λ=βtx, is explored. Large sample and exact significance tests are developed to test the relationship between survival and one covariate. The standard unconditional likelihood approach is compared with two new tests, which are based on the conditional distribution of the sufficient statistics. The various testing procedures are compared numerically. Finally, the relationship with the semiparametric proportional hazard model is investigated.
|Number of pages||11|
|Journal||Journal of Statistical Planning and Inference|
|State||Published - 1 Jul 2000|
Bibliographical noteFunding Information:
This work was partially supported by grants from the National Cancer Institute and National Institutes of Health.
- Conditional inference
- Exact tests
- Exponential regression
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Applied Mathematics