We show how to combine censoring technique for Markov chain and strong stochastic comparison to obtain bounds on rewards and the first passage time. We present the main ideas of the method, the algorithms and their proofs. We obtain a substantial reduction of the state space due to the censoring technique. We also present some numerical results to illustrate the effectiveness of the method.

We consider the problem of estimation of the parameters of the Marshall-Olkin Bivariate Weibull distribution in the presence of random censoring. Since the maximum likelihood estimators of the parameters can not be expressed in a closed form, we suggest an EM algorithm to compute the same. Extensive simulations are done to conclude that the estimators perform efficiently under random censoring.

Prediction on the basis of censored data is very important topic in many fields including medical and engineering sciences. In this paper, based on progressive Type-II right censoring scheme, we will discuss Bayesian two-sample prediction. A general form for lifetime model including some well known and useful models such asWeibull and Pareto is considered for obtaining prediction bounds ...

Consider critical percolation on a rhombus in the hexagonal lattice, and let A the event of a left to right crossing (by 1’s). Suppose we sample configurations from A by starting from some fixed configuration in A, and then at each step a uniformly random hexagon is resampled as long as the resulting configuration is in A. It is easy to see that this sampling procedure converges to the uniform ...

A new Type-II generalized progressively hybrid censoring strategy, in which the experiment is ensured to stop at a specified time, explored when lifetime model of test subjects follows two-parameter alpha-power inverted exponential (Alpha-PIE) distribution. Alpha-PIE’s parameters and reliability indices, such as hazard rate functions, are estimated via maximum likelihood Bayes estimation method...

Multinomial Choice: Data consists of consumer choices of various goods, along char­ acteristics. Let there be J choices and y = (y1, . . . , yJ ) where yj = 1 if good j is chosen and yj = 0 otherwise. Let x be observed characteristics of the goods and choices. Here a conditional density for y corresponds to conditional choice probabilities P (j|x, β), one for each j, with �J j=1 P (j|x, β) = 1 ...