In this paper, we obtain ordering properties for coherent systems with possibly dependent identically distributed components. These results are based on a representation of the system reliability function as a distorted function of the common component reliability function. So, the results included in this paper can also be applied to general distorted distributions. The main advantage of these results is that they are distribution-free with respect to the common component distribution. Moreover, they can be applied to systems with component lifetimes having a non-exchangeable joint distribution.
Appl. Stochastic ModelsBus. Ind. 2013, 29 264-278 J. NAVARRO ET AL.The rest of the paper is organized as follows. In Section 2, we give the main results of the paper with the general ordering results for distorted distributions and coherent systems with ID components. Specifically, we study properties of the usual stochastic order, hazard rate order, the reversed hazard rate order, the likelihood ratio order, the increasing convex order, the total time on test transform order, and the excess wealth order. In Section 3, we give some examples to show how to apply our general results to specific systems. These examples include systems with IID components and systems with dependent ID components. In Section 4, we give some conclusions and open questions for future research.Throughout the paper, we use the terms increasing and decreasing in a wide sense, that is, a function g is increasing (decreasing) if g.x/ 6 g.y/.>/ for all x 6 y. Whenever we consider an expectation (or a conditional random variable), we assume that it exists.
The main resultsLet us consider a coherent system with lifetime T D .X 1 ; : : : ; X n / based on possibly dependent components with lifetimes X 1 ; : : : ; X n , where is the structure function (see [29], Chapter 1). Let us assume that X 1 ; : : : ; X n are identically distributed (ID) with a common reliability function F .t/ D Pr.X i > t/ for i D 1; : : : ; n. The component lifetimes X 1 ; : : : ; X n can be dependent, and this dependence will be represented by the joint reliability (or survival) function of .X