【摘要】：本文從分布函數(shù)與樣本觀測值數(shù)據(jù)兩方面探討模糊隨機變量的相依性。若已知模糊隨機變量左右端點值分布函數(shù)，通過copula函數(shù)理論從分布函數(shù)建立模糊copula函數(shù)模型求出模糊隨機變量的聯(lián)合分布函數(shù)；若無法已知模糊隨機變量的分布函數(shù)，，以kendall統(tǒng)計量為基礎(chǔ)，從一致相依性角度對二模糊隨機變量進行相依性度量。通過模糊排序方法提出模糊一致相依性度量參數(shù)；證明Kendall統(tǒng)計量是相依性度量參數(shù)的一個無偏估計。建立模糊kendall統(tǒng)計量作為模糊隨機變量之間的相依性度量工具。 最后通過模糊隨機變量的相依性度量工具探討保險業(yè)務(wù)中個體風(fēng)險和聚合風(fēng)險模型。
[Abstract]:In this paper, the dependence of fuzzy random variables is discussed in terms of distribution function and sample observation data. If the distribution function of left and right endpoints of fuzzy random variables is known, the joint distribution function of fuzzy random variables can be obtained from the model of fuzzy copula function by copula function theory, if the distribution function of fuzzy random variables cannot be known, Based on kendall statistics, this paper measures the dependence of two fuzzy random variables from the point of view of uniform dependence. The fuzzy uniform dependency metric parameter is proposed by fuzzy sorting method, and it is proved that the Kendall statistic is an unbiased estimate of the dependency metric parameter. A fuzzy kendall statistic is established as a tool for measuring the dependence of fuzzy random variables. Finally, the model of individual risk and aggregate risk in insurance business is discussed by means of the dependency measurement tool of fuzzy random variables.
【學(xué)位授予單位】：廣州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：O211.5;F840

【參考文獻】

相關(guān)期刊論文 前1條

1 唐家銀;何平;;基于Copula對隨機變量間相依性的度量[J];江漢大學(xué)學(xué)報(自然科學(xué)版);2006年04期

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