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ASYMPTOTIC INFERENCE FOR A CLASS OF CHAIN BINOMIAL MODELS
Author(s) -
Huggins R.M.
Publication year - 1993
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1993.tb01314.x
Subject(s) - negative binomial distribution , binomial distribution , mathematics , poisson distribution , estimator , binomial (polynomial) , negative multinomial distribution , statistics , inference , quasi likelihood , beta binomial distribution , class (philosophy) , chain (unit) , poisson binomial distribution , count data , multinomial distribution , asymptotic distribution , computer science , artificial intelligence , physics , astronomy
Summary Chain binomial models axe commonly used to model the spread of an epidemic through a population. This paper shows that for a flexible class of chain binomial models an approximate maximum likelihood estimator of the infection rate, derived from a Poisson approximation to the binomial distribution, has an asymptotically normal distribution, as do some other related'estimators.