Fixed accuracy estimation for chain binomial models

R. M. Huggins

Stochastic Processes and their Applications, 1992, vol. 41, issue 2, 273-280

Abstract: In many epidemic models the initial infection rate, suitably defined, plays a major role in determining the probability of an outbreak of a disease becoming a major epidemic. Here we model the epidemic as a chain binomial model and consider an approximate maximum likelihood estimator of the infection rate. It is shown that under mild conditions sampling according to a simple stopping rule yields an asymptotically normally distributed estimator which may be computed during the course of an epidemic. A small simulation study suggests that the asymptotic results applied to small samples yield accurate confidence intervals.

Keywords: stopping; rule; ficed; width; confidence; interval; asymptotic; normality (search for similar items in EconPapers)
Date: 1992
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