General limited information diffusion method of small-sample information analysis in insurance
When analyzing and evaluating risks in insurance,people are often confronted with the situation of incomplete information and insufficient data,which is known as a small-sample problem.In this paper,a one-dimensional small-sample problem in insurance was investigated using the kernel density estimation method(KerM)and general limited information diffusion method(GIDM).In particular,MacCormack technique was applied to get the solutions of GIDM equations and then the optimal diffusion solution was acquired based on the two optimization principles.Finally,the analysis introduced in this paper was verified by treating some examples and satisfying results were obtained.
fuzzy mathematicskernel density estimationinformation diffusionMacCormack techniquesmall-sample
忻莉莉、耿辉、王永民、张晶晶
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Department of Mathematics,College of Sciences,Shanghai University,Shanghai 200444,P.R.China
fuzzy mathematics kernel density estimation information diffusion MacCormack technique small-sample
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