In this paper we specify the conditions on the parameters of pairs of gOS’s under which the corresponding generalized order statistics are ordered according to usual stochastic ordering, hazard rate ordering, likelihood ratio ordering and dispersive ordering. We consider this problem in one-sample as well as two-sample problems. We show that some of the results obtained by Franco et al. ...

 In a number of life-testing experiments, there exist situations where the monitoring breaks down for a temporary period of time. In such cases, some parts of the ordered observations, for example the middle ones, are censored and the only outcomes available for analysis consist of the lower and upper order statistics. Therefore, the experimenter may not gain the complete information on fa...

let x1;x2;...;xn have a jointly multivariate exchangeable normal distribution. in this work we investigate another proof of the independence of x and s2 using order statistics. we also assume that (xi ; yi); i =1; 2;...; n; jointly distributed in bivariate normal and establish the independence of the mean and the variance of concomitants of order statistics.

in this paper, we derive the exact analytical expressions for the shannon entropy of generalized orderstatistics from pareto-type and related distributions.

We will analyze the number of comparisons needed to solve the problem of sorting n distinct items into ascending order, and the related problem of selecting the kth smallest item from an unsorted list of n items. Section 1 examines mergesort, an algorithm with excellent worst case performance, and Section 2 examines quicksort, an algorithm with excellent average case performance. Section 3 cont...

Sample estimates of moments and cumulants are known to be unstable in the presence of outliers. This problem is especially severe for higher order statistics, like kurtosis, which are used in algorithms for independent components analysis and projection pursuit. In this paper we propose robust generalizations of moments and cumulants that are more insensitive to outliers but at the same time re...

In this paper, assuming that (X, Y1, Y2)T has a trivariate normal distribution, we derive the exact joint distribution of ( X, Y(1), Y(2))^T, where Y(1) and Y(2) are order statistics arising from (Y1, Y2)T . We show that this joint distribution is a mixture of truncated trivariate normal distributions and then use this mixture representation to derive the best (nonlinear) predictiors of X...