data envelopment analysis (dea) is a method for measuring the relative efficiencies of a set of decision-making units (dmus) that use multiple inputs to produce multiple outputs. in this paper, we study the measurement of dmu performances in dea in situations where input and/or output values are given as imprecise data. by imprecise data we mean situations where we only know that the actual val...

background: in recent years, the study of birth intervals has been a main determinant of the levels of fertility in the populations, as it is associated with rates of fertility and population growth. objective: the purpose of this study was to firstly apply survival analysis for modeling of first birth interval and secondly to explore its determinants. materials and methods: in a cross sectiona...

this paper proposes an alternative approach for efficiency analysis when a set of dmus uses interval scale variables in the productive process. to test the influence of these variables, we present a general approach of deriving dea models to deal with the variables. we investigate a number of performance measures with unrestricted-in-sign interval and/or interval scale variables.

one-way analysis of covariance is a popular and common statisticalmethod, wherein the equality of the means of several random variables whichhave a linear relationship with a random mathematical variable, is tested. inthis study, a method is presented to improve the one-way analysis of covari-ance when there is an uncertainty in accepting the statistical hypotheses. themethod deals with a fuzzy...

data envelopment analysis (dea) is a technique for measuring the efficiency of decision making units. in all models of the dea, for each unit under assessment, the numerical efficiency is obtained which may be less than or equal to one. given the possible large number of functional units, we use various ranking methods for evaluating units. one of the rating methods is balance index and topsis....