The median of a random fuzzy number. The 1-norm distance approach

In quantifying the central tendency of the distribution of a random fuzzy number (or fuzzy random variable in Puri and Ralescu's sense), the most usual measure is the Aumann-type mean, which extends the mean of a real-valued random variable and preserves its main properties and behavior. Although such a behavior has very valuable and convenient implications, 'extreme' values or changes of data entail too much influence on the Aumann-type mean of a random fuzzy number. This strong influence motivates the search for a more robust central tendency measure. In this respect, this paper aims to explore the extension of the median to random fuzzy numbers. This extension is based on the 1-norm distance and its adequacy will be shown by analyzing its properties and comparing its robustness with that of the mean both theoretically and empirically.

[1]  D. Dubois,et al.  Systems of linear fuzzy constraints , 1980 .

[2]  Frank Proske,et al.  A strong law of large numbers for generalized random sets from the viewpoint of empirical processes , 2003 .

[3]  M. Puri,et al.  Fuzzy Random Variables , 1986 .

[4]  Michael Smithson,et al.  Applications of fuzzy set concepts to behavioral sciences , 1982, Math. Soc. Sci..

[5]  Ana Colubi,et al.  A fuzzy representation of random variables: An operational tool in exploratory analysis and hypothesis testing , 2006, Comput. Stat. Data Anal..

[6]  Ana Colubi,et al.  Bootstrap techniques and fuzzy random variables: Synergy in hypothesis testing with fuzzy data , 2006, Fuzzy Sets Syst..

[7]  M. Puri,et al.  Limit theorems for fuzzy random variables , 1986, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[8]  Ronald R. Yager,et al.  A procedure for ordering fuzzy subsets of the unit interval , 1981, Inf. Sci..

[9]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[10]  Emmanuel Valvis A new linear ordering of fuzzy numbers on subsets of F (\pmb\mathbb R ). , 2009 .

[11]  Ana Colubi,et al.  A generalized strong law of large numbers , 1999 .

[12]  Yukio Ogura,et al.  Strong laws of large numbers for independent fuzzy set-valued random variables , 2006, Fuzzy Sets Syst..

[13]  C. Castaing,et al.  Convex analysis and measurable multifunctions , 1977 .

[14]  María Asunción Lubiano,et al.  SAFD — An R Package for Statistical Analysis of Fuzzy Data , 2013 .

[15]  Wolfgang Näther,et al.  On the variance of random fuzzy variables , 2002 .

[16]  Ana Colubi,et al.  On the formalization of fuzzy random variables , 2001, Inf. Sci..

[17]  María Asunción Lubiano,et al.  Two-sample hypothesis tests of means of a fuzzy random variable , 2001, Inf. Sci..

[18]  Ana Colubi,et al.  A _{}[0,1] representation of random upper semicontinuous functions , 2002 .

[19]  Ilya Molchanov On strong laws of large numbers for random upper semicontinuous functions , 1999 .

[20]  P. Kloeden,et al.  Metric spaces of fuzzy sets , 1990 .

[21]  J. Ramík,et al.  Inequality relation between fuzzy numbers and its use in fuzzy optimization , 1985 .

[22]  Vladik Kreinovich,et al.  Fuzzy numbers are the only fuzzy sets that keep invertible operations invertible , 1997, Fuzzy Sets Syst..

[23]  Arnold F. Shapiro,et al.  An application of fuzzy random variables to control charts , 2010, Fuzzy Sets Syst..

[24]  Ricardo Fraiman,et al.  On the use of the bootstrap for estimating functions with functional data , 2006, Comput. Stat. Data Anal..

[25]  Ana Colubi,et al.  Bootstrap approach to the multi-sample test of means with imprecise data , 2006, Comput. Stat. Data Anal..

[26]  Ana Colubi,et al.  Asymptotic and Bootstrap techniques for testing the expected value of a fuzzy random variable , 2004 .

[27]  Yu-Cheng Lee,et al.  Service quality gaps analysis based on Fuzzy linguistic SERVQUAL with a case study in hospital out‐patient services , 2010 .

[28]  Emmanuel Valvis,et al.  A new linear ordering of fuzzy numbers on subsets of $${{\mathcal F}({\pmb{\mathbb{R}}}})$$ , 2009, Fuzzy Optim. Decis. Mak..

[29]  M. Hukuhara INTEGRATION DES APPLICAITONS MESURABLES DONT LA VALEUR EST UN COMPACT CONVEXE , 1967 .

[30]  M. Puri,et al.  The Concept of Normality for Fuzzy Random Variables , 1985 .

[31]  P. Kloeden,et al.  Metric Spaces of Fuzzy Sets: Theory and Applications , 1994 .

[32]  Ana Colubi,et al.  A new family of metrics for compact, convex (fuzzy) sets based on a generalized concept of mid and spread , 2009, Inf. Sci..

[33]  D. Gervini Robust functional estimation using the median and spherical principal components , 2008 .

[34]  D. Luenberger Optimization by Vector Space Methods , 1968 .

[35]  Ana Colubi,et al.  Computational Statistics and Data Analysis Fuzzy Data Treated as Functional Data: a One-way Anova Test Approach , 2022 .

[36]  R. Fraiman,et al.  Trimmed means for functional data , 2001 .

[37]  Ralf Körner An asymptotic α-test for the expectation of random fuzzy variables , 2000 .