A (univariate) random variable is said to be of phase type if it can be represented as the time until absorption in a finite state absorbing Markov chain. Univariate phase type random variables are useful because they arise from processes that are often encountered in applications, they have densities that can be written in a closed form, they possess some useful closure properties, and they can approximate any nonnegative random variable. This paper introduces and discusses several extensions to the multivariate case. It shows that the multivariate random variables possess many of the properties of univariate phase type distributions and derives explicit formulas for various probabilistic quantities of interest. Some examples are included.
We consider a financial market model with two assets. One has deterministic rate of growth, while the rate of growth of the second asset is governed by a Brownian motion with drift. We can shift money from one asset to another; however, there are losses of money (brokerage fees) involved in shifting money from the risky to the nonrisky asset. We want to maximize the expected rate of growth of funds. It is proved that an optimal policy keeps the ratio of funds in risky and nonrisky assets within a certain interval with minimal effort.
n candidates, represented by n i.i.d. continuous random variables X 1, …, Xn with known distribution arrive sequentially, and one of them must be chosen, using a non-anticipating stopping rule. The objective is to minimize the expected rank (among the ranks of X 1, …, Xn ) of the candidate chosen, where the best candidate, i.e. the one with smallest X-value, has rank one, etc. Let the value of the optimal rule be Vn , and lim Vn = V. We prove that V > 1.85. Limiting consideration to the class of threshold rules of the form tn = min {k: Xk ≦ ak for some constants ak , let Wn be the value of the expected rank for the optimal threshold rule, and lim Wn = W. We show 2.295 < W < 2.327.
Optimum group maintenance policies for a set of N machines subjected to stochastic failures under continuous and periodic inspections are considered. Under very general conditions it is shown that a control limit policy minimizes the expected cost per unit time over an infinite horizon, when costs are incurred due to loss of production and repair only. It is also shown how to explicitly compute this control limit. When additional costs are incurred due to inspection, a characterization of an optimal periodic inspection/repair policy that minimizes the expected cost per unit time over an infinite horizon is given.group maintenance, continuous inspection, periodic inspection
Let X i ≥ 0 be independent, i = 1,…, n, with known distributions and let X n *= max(X 1,…,X n ). The classical ‘ratio prophet inequality’ compares the return to a prophet, which is EX n *, to that of a mortal, who observes the X i s sequentially, and must resort to a stopping rule t. The mortal's return is V(X 1,…,X n ) = max EX t , where the maximum is over all stopping rules. The classical inequality states that EX n * < 2V(X 1,…,X n ). In the present paper the mortal is given k ≥ 1 chances to choose. If he uses stopping rules t 1,…,t k his return is E(max(X t 1,…,X t k )). Let t(b) be the ‘simple threshold stopping rule’ defined to be the smallest i for which X i ≥ b, or n if there is no such i. We show that there always exists a proper choice of k thresholds, such that EX n * ≤ ((k+1)/k)Emax(X t 1,…,X t k )), where t i is of the form t(b i ) with some added randomization. Actually the thresholds can be taken to be thej/(k+1) percentile points of the distribution of X n *, j = 1,…,k, and hence only knowledge of the distribution of X n * is needed.
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