One-Factor ANOVA Model Using Trapezoidal Fuzzy Numbers Through Alpha Cut Interval Method

Most of our traditional tools in descriptive and inferential statistics is based on crispness (preciseness) of data, measurements, random variable, hypotheses, and so on.  By crisp we mean dichotomous that is, yes-or-no type rather than more-or-less type.  But there are many situations in which the above assumptions are rather non-realistic such that we need some new tools to characterize and analyze the problem.  By introducing fuzzy set theory, different branches of mathematics are recently studied.  But probability and statistics attracted more attention in this regard because of their random nature.  Mathematical statistics does not have methods to analyze the problems in which random variables are vague (fuzzy). In this regard, a simple and new technique for testing the hypotheses under the fuzzy environments is proposed.  Here, the employed data are in terms of trapezoidal fuzzy numbers (TFN) which have been transformed into interval data using  interval method and on the grounds of the transformed fuzzy data, the one-factor ANOVA test is executed and decisions are concluded.  This concept has been illustrated by giving two numerical examples. Keywords: Fuzzy set, , Trapezoidal fuzzy number (TFN), Test of hypotheses, One-factor ANOVA model, Upper level data, Lower level data

A Comparative Study of Chi-Square Goodness-of-Fit Under Fuzzy Environments

Testing goodness-of-fit plays a vital role in data analysis.  This problem seems to be much more complicated in the presence of vague data.  In this paper, the chi-square goodness-of-fit under trapezoidal fuzzy numbers (tfns.) is proposed using alpha cut interval method.  And the ranking grades of tfns. are also used to compute the chi-square test statistic.  The proposed technique is illustrated with two different numerical examples along with different methods of ranking grades for a concrete comparative study. Keywords: Chi-square Test, Fuzzy Sets, Trapezoidal Fuzzy Numbers, Alpha Cut, Ranking Function, Graded Mean Integration Representation

Testing fuzzy hypotheses using fuzzy data based on fuzzy test statistic

Abstract This paper deals with the problem of testing hypothesis when both the hypotheses and the available data are fuzzy. First, four different kinds of fuzzy hypotheses are defined. Then, a procedure is developed for constructing the fuzzy point estimation based on fuzzy data. Also, the concept of fuzzy test statistic is defined based on the α-cuts of the fuzzy null hypothesis and the α-cuts of the constructed fuzzy point estimation. Finally, by introducing a credit level, we propose a method to evaluate the fuzzy hypotheses of interest. The proposed method is employed to test the fuzzy hypotheses for the mean of a normal distribution, and the variance of a normal distribution. A practical example in lifetime testing is provided, to show the applicability of the proposed method in applied studies

Statistische Tests bei Unschärfe

Statistische Tests beim Vorliegen unscharfer Daten (Fuzzy-Daten) und zum Testen unscharfer Hypothesen werden untersucht. Tests für den (unscharfen) Erwartungswert einer unscharfen Zufallsvariable (Fuzzy-Zufallsvariable) werden konstruiert. Die Gütefunktionen werden zum Vergleich der verschiedenen Tests bestimmt. Die angegebenen Tests sind dabei zum Teil optimal. Es wird aufgezeigt, wann bei Tests für scharfe Daten deren Verunschärfung nicht mit in die Testentscheidung einbezogen werden muss und wann die Einbeziehung zu einer Verbesserung der Testentscheidung führt. Weiter wird gezeigt, wie und wann die Erweiterung der Teststatistik, des P-Wertes und des zum Test gehörigen Konfidenzintervalles zur gleichen unscharfen Testfunktion führen. Beim Testen unscharfer Hypothesen werden klassische Begriffe wie maximale Wahrscheinlichkeit für den Fehler 1.Art zum einen scharf zum anderen unscharf verallgemeinert. Für beide Fälle ist, mit diesen verallgemeinerten Begriffen und unter gewissen Voraussetzungen, ein optimaler Test bestimmbar