A Fuzzy Random Variable Approach to Life Insurance Pricing

2012 ◽  
pp. 111-125
Author(s):  
Jorge de Andrés-Sánchez ◽  
Laura González-Vila Puchades
Keyword(s):  
Life Insurance ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Insurance Pricing ◽  
Variable Approach ◽  
Fuzzy Random

scholarly journals On Distribution Characteristics of a Fuzzy Random Variable

2018 ◽  
Vol 47 (2) ◽  
pp. 53-67 ◽  
Author(s):  
Jalal Chachi
Keyword(s):  
Probability Theory ◽  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Random Variables ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Cumulative Distribution ◽  
Distribution Characteristics ◽  
Fuzzy Random Variables ◽  
Fuzzy Random

In this paper, rst a new notion of fuzzy random variables is introduced. Then, usingclassical techniques in Probability Theory, some aspects and results associated to a randomvariable (including expectation, variance, covariance, correlation coecient, etc.) will beextended to this new environment. Furthermore, within this framework, we can use thetools of general Probability Theory to dene fuzzy cumulative distribution function of afuzzy random variable.

The Compatibility Assessment between Voltage Sags and Equipment Tolerance Based on Fuzzy-Random Method

2012 ◽  
Vol 588-589 ◽  
pp. 458-462
Author(s):  
Zhi Jian Yuan ◽  
Yan Li
Keyword(s):  
Engineering Application ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Assessment Model ◽  
Voltage Sags ◽  
Equipment Failure ◽  
Failure Event ◽  
Cut Set ◽  
The Impact ◽  
Fuzzy Random

The impact of voltage sags on equipment is usually described by equipment failure probability.It is generally difficult to assess and predict the probability because of the uncertainty of both the nature of voltage sags and the VTL (VTL) of equipment. By defining the equipment failure event caused by voltage sags as a fuzzy-random event, a fuzzy-random assessment model incorporating those uncertainty is developed. The model is able to convert the probability problem of a fuzzy-random variable to that of a common random variable by using λ-cut set. It is thus valuable in theoretical analysis and engineering application. The validity of the developed model is verified by Monte Carlo stochastic simulation using personal computers (PCs)as test equipment.

scholarly journals On the Generalization Performance of a Regression Model with Imprecise Elements

2017 ◽  
Vol 25 (05) ◽  
pp. 723-740 ◽  
Author(s):  
Maria Brigida Ferraro
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Prediction Error ◽  
Random Element ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Generalization Performance ◽  
Bootstrap Procedure ◽  
Link Functions ◽  
Fuzzy Random

A linear regression model for imprecise random variables is considered. The imprecision of a random element has been formalized by means of the LR fuzzy random variable, characterized by a center, a left and a right spread. In order to avoid the non-negativity conditions the spreads are transformed by means of two invertible functions. To analyze the generalization performance of that model an appropriate prediction error is introduced, and it is estimated by means of a bootstrap procedure. Furthermore, since the choice of response transformations could affect the inferential procedures, a computational proposal is introduced for choosing from a family of parametric link functions, the Box-Cox family, the transformation parameters that minimize the prediction error of the model.

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