Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 6 Articles
We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure.\nThe commonly adopted Bayesian setup involves the conjugate prior,multivariate normal distribution for the regression coefficients\nand inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian\nestimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix\nis considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs\nin prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure.\nThe posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte\nCarlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density\nthat closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a\nmultiple regression based upon the 1980 High School and Beyond Survey....
The family of consecutive-type reliability systems is under investigation.More specifically, an up-to-date presentation of almost all\ngeneralizations of the well-known consecutive k-out-of-n: F system that have been proposed in the literature is displayed, while\nseveral recent and fundamental results for each member of the aforementioned family are stated....
Some generalized integral inequalities are established for the fractional expectation and the fractional variance for continuous\nrandom variables. Special cases of integral inequalities in this paper are studied by Barnett et al. and Dahmani....
We derive the moderate and large deviations principle for the smoothed sample quantile from a sequence of independent and\nidentically distributed samples of size n....
Consider the single server queue in which the system capacity is infinite and the customers are served on a first come, first served\nbasis. Suppose the probability density function f(t) and the cumulative distribution function f(t) of the interarrival time are such\nthat the rate f(t)/ [1 ? F(T)] tends to a constant asT ? ?, and the rate computed fromthe distribution of the service time tends to\nanother constant.When the queue is in a stationary state, we derive a set of equations for the probabilities of the queue length and\nthe states of the arrival and service processes. Solving the equations, we obtain approximate results for the stationary probabilities\nwhich can be used to obtain the stationary queue length distribution and waiting time distribution of a customer who arrives when\nthe queue is in the stationary state....
Advancement in technology has led to greater accessibility of massive and complex data in many fields such as quality and\nreliability. The proper management and utilization of valuable data could significantly increase knowledge and reduce cost\nby preventive actions, whereas erroneous and misinterpreted data could lead to poor inference and decision making. On the\nother side, it has become more difficult to process the streaming high-dimensional time-to-event data in traditional application\napproaches, specifically in the presence of censored observations. This paper presents a multipurpose analytic model and practical\nnonparametric methods to analyze right-censored time-to-event data with high-dimensional covariates. In order to reduce\nredundant information and to facilitate practical interpretation, variable inefficiency in failure time is determined for the specific\nfield of application. To investigate the performance of the proposed methods, these methods are compared with recent relevant\napproaches through numerical experiments and simulations....
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