In recent years, online social networks (OSN) have emerged as a platform of sharing variety of information about people, and their interests, activities, events and news from real worlds. Due to the large scale and access limitations (e.g., privacy policies) of online social network services such as Facebook and Twitter, it is difficult to access the whole public network in a limited amount of ...

BACKGROUND Nonrandom sampling of populations in developing nations has limitations and can inaccurately estimate health phenomena, especially among hard-to-reach populations such as rural residents. However, random sampling of rural populations in developing nations can be challenged by incomplete enumeration of the base population. METHODS We describe a stratified random sampling method usin...

Generation of deviates from random graph models with non-trivial edge dependence is an increasingly important problem. Here, we introduce a method which allows perfect sampling from random graph models in exponential family form (“exponential family random graph” models), using a variant of Coupling From The Past. We illustrate the use of the method via an application to the Markov graphs, a fa...

We discuss in this paper statistical inference of sample average approximations of multistage stochastic programming problems. We show that any random sampling scheme provides a valid statistical lower bound for the optimal value of the true problem. However, in order for such lower bound to be consistent one needs to employ the conditional sampling procedure. We also indicate that fixing a fea...

We discuss in this paper statistical inference of sample average approximations of multistage stochastic programming problems. We show that any random sampling scheme provides a valid statistical lower bound for the optimal (minimum) value of the true problem. However, in order for such lower bound to be consistent one needs to employ the conditional sampling procedure. We also indicate that fi...

Bayesian Acceptance Sampling Approach is associated with utilization of prior process history for the selection of Distributions (viz., Gamma Poisson, Beta Binomial) to describe the random fluctuations involved in Acceptance Sampling. The Acceptance sampling procedures are the practical tools for quality assurance applications involving product control. Acceptance sampling systems are advocated...

When analyzing spatial databases or other datasets with spatial attributes, one frequently wants to cluster the data according to spatial attributes. In this paper, we describe a novel density-based spatial clustering method called DBRS. The algorithm can identify clusters of widely varying shapes, clusters of varying densities, clusters which depend on non-spatial attributes, and approximate c...