Bootstrap techniques and fuzzy random variables: Synergy in hypothesis testing with fuzzy data

2006 ◽  
Vol 157 (19) ◽  
pp. 2608-2613 ◽  
Author(s):  
Gil González-Rodríguez ◽  
Manuel Montenegro ◽  
Ana Colubi ◽  
María Ángeles Gil
Keyword(s):  
Hypothesis Testing ◽  
Random Variables ◽  
Fuzzy Data ◽  
Fuzzy Random Variables ◽  
Bootstrap Techniques ◽  
Fuzzy Random

scholarly journals A fuzzy nonparametric Shewhart chart based on the bootstrap approach

2015 ◽  
Vol 25 (2) ◽  
pp. 389-401 ◽  
Author(s):  
Dabuxilatu Wang ◽  
Olgierd Hryniewicz
Keyword(s):  
Random Variables ◽  
Fuzzy Data ◽  
Fuzzy Random Variables ◽  
Test Statistic ◽  
Shewhart Chart ◽  
Novel Approach ◽  
Shewhart Control ◽  
Bootstrap Methodology ◽  
Control Limits ◽  
Fuzzy Random

Abstract In this paper, we consider a nonparametric Shewhart chart for fuzzy data. We utilize the fuzzy data without transforming them into a real-valued scalar (a representative value). Usually fuzzy data (described by fuzzy random variables) do not have a distributional model available, and also the size of the fuzzy sample data is small. Based on the bootstrap methodology, we design a nonparametric Shewhart control chart in the space of fuzzy random variables equipped with some L2 metric, in which a novel approach for generating the control limits is proposed. The control limits are determined by the necessity index of strict dominance combined with the bootstrap quantile of the test statistic. An in-control bootstrap ARL of the proposed chart is also considered.

scholarly journals Several Limit Theorems on Fuzzy Quantum Space

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 438
Author(s):  
Viliam Ďuriš ◽  
Renáta Bartková ◽  
Anna Tirpáková
Keyword(s):  
Financial Markets ◽  
Incomplete Data ◽  
Extreme Values ◽  
Random Variables ◽  
Consumer Electronics ◽  
Financial Risks ◽  
Quantum Space ◽  
Fuzzy Random Variables ◽  
Scientific Disciplines ◽  
Fuzzy Random

The probability theory using fuzzy random variables has applications in several scientific disciplines. These are mainly technical in scope, such as in the automotive industry and in consumer electronics, for example, in washing machines, televisions, and microwaves. The theory is gradually entering the domain of finance where people work with incomplete data. We often find that events in the financial markets cannot be described precisely, and this is where we can use fuzzy random variables. By proving the validity of the theorem on extreme values of fuzzy quantum space in our article, we see possible applications for estimating financial risks with incomplete data.

scholarly journals Fuzzy random variables

1986 ◽  
Vol 114 (2) ◽  
pp. 409-422 ◽  
Author(s):  
Madan L Puri ◽  
Dan A Ralescu
Keyword(s):  
Random Variables ◽  
Fuzzy Random Variables ◽  
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.

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