The Annals of Statistics

Maximum Likelihood Estimation of Dose-Response Functions Subject to Absolutely Monotonic Constraints

H. A. Guess and K. S. Crump

Full-text: Open access

Abstract

Statistical properties are derived for maximum likelihood estimates of dose-response functions in which the response probability is related to the dose by means of a polynomial of unknown degree with nonnegative coefficients. Dose-response functions of this form are predicted by the multistage model of carcinogenesis. We first establish necessary and sufficient conditions for strong consistency of the estimates. For these results no assumptions are made about the polynomial degree, so the number of coefficients to be estimated is effectively infinite. Under some additional assumptions, which do involve restrictions on the polynomial degree, we obtain the asymptotic distribution of the vector of maximum likelihood estimates about the true vector of polynomial coefficients. Because the coefficients are constrained to be nonnegative, the limiting distribution will generally not be normal.

Article information

Source
Ann. Statist., Volume 6, Number 1 (1978), 101-111.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176344069

Digital Object Identifier
doi:10.1214/aos/1176344069

Mathematical Reviews number (MathSciNet)
MR458771

Zentralblatt MATH identifier
0371.62147

JSTOR
links.jstor.org

Subjects
Primary: 62P10: Applications to biology and medical sciences
Secondary: 60F15: Strong theorems 62E20: Asymptotic distribution theory

Keywords
Absolutely monotonic functions cancer risk estimation constrained maximum likelihood estimates convergence in distribution dose-response functions strong consistency Tchebycheff systems

Citation

Guess, H. A.; Crump, K. S. Maximum Likelihood Estimation of Dose-Response Functions Subject to Absolutely Monotonic Constraints. Ann. Statist. 6 (1978), no. 1, 101--111. doi:10.1214/aos/1176344069. https://projecteuclid.org/euclid.aos/1176344069


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