Exact tests for exponential regression

Ori Davidov; Marvin Zelen

doi:10.1016/s0378-3758(99)00202-5

Exact tests for exponential regression

Ori Davidov, Marvin Zelen

Department of Statistics

Research output: Contribution to journal › Article › peer-review

Abstract

The exponential distribution is a fundamental model in parametric survival analysis. In this paper the exponential regression model with a canonical parameterization, λ=β^tx, 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.

Original language	English
Pages (from-to)	87-97
Number of pages	11
Journal	Journal of Statistical Planning and Inference
Volume	88
Issue number	1
DOIs	https://doi.org/10.1016/s0378-3758(99)00202-5
State	Published - 1 Jul 2000

Bibliographical note

Funding Information:
This work was partially supported by grants from the National Cancer Institute and National Institutes of Health.

Keywords

62J05
62J12
Conditional inference
Exact tests
Exponential regression

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1016/s0378-3758(99)00202-5

Cite this

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KW - Conditional inference

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KW - Exponential regression

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ER -

Exact tests for exponential regression

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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