Constructing likelihood functions for interval‐valued random variables

Dominated Convergence for Fuzzy Random Variables

SOME PROBABILISTIC INEQUALITIES FOR FUZZY RANDOM VARIABLES

In this paper, the concepts of positive dependence and linearlypositive quadrant dependence are introduced for fuzzy random variables. Also,an inequality is obtained for partial sums of linearly positive quadrant depen-dent fuzzy random variables. Moreover, a weak law of large numbers is estab-lished for linearly positive quadrant dependent fuzzy random variables. Weextend some well known inequ...

Maximal Inequalities for Associated Random Variables

In a celebrated work by Shao [13] several inequalities for negatively associated random variables were proved. In this paper we obtain some maximal inequalities for associated random variables. Also we establish a maximal inequality for demimartingales which generalizes and improves the result of Christofides [4].

Error-in-variables likelihood functions for motion estimation

Over-determined linear systems with noise in all measurements are common in computer vision, and particularly in motion estimation. Maximum likelihood estimators have been proposed to solve such problems, but except for simple cases, the corresponding likelihood functions are extremely complex, and accurate confidence measures do not exist. This paper derives the form of simple likelihood funct...

Random variables, joint distribution functions, and copulas

If G is an n-dimensional joint distribution function with 1-dimensional margins F1,...,Fn, then there exists a function C (called an "w-copula") from the unit »-cube to the unit interval such that G(xx, ..., xn) = C(F1(x1), ..., Fn(xn)) for all real n-tuples (xlt ..., xn). The paper is devoted to an investigation of the structure and properties of n-copulas and their connection with random vari...