Applying the saddlepoint approximation to bivariate stochastic processes.
The problem of moment closure is central to the study of multitype stochastic population dynamics since equations for moments up to a given order will generally involve higher-order moments. To obtain a Normal approximation, the standard approach is to replace third- and higher-order moments by zero, which may be severely restrictive on the structure of the p.d.f. The purpose of this paper is therefore to extend the univariate truncated saddlepoint procedure to multivariate scenarios. This has several key advantages: no distributional assumptions are required; it works regardless of the moment order deemed appropriate; and, we obtain an algebraic form for the associated p.d.f. irrespective of whether or not we have complete knowledge of the cumulants. The latter is especially important, since no families of distributions currently exist which embrace all cumulants up to any given order. In general the algorithm converges swiftly to the required p.d.f.; analysis of a severe test case illustrates its current operational limit.
Renshaw
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Department of Statistics and Modelling Science, University of Strathclyde, Glasgow, UK. eric@stams.strath.ac.uk
D000465:Algorithms; D000818:Animals; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D015999:Multivariate Analysis; D009716:Numerical Analysis, Computer-Assisted; D016012:Poisson Distribution; D011157:Population Dynamics; D013269:Stochastic Processes