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The Naïve Bayes is a tractable and efficient approach for statistical classification. In general classification problems, the consequences of misclassifications may be rather different in different classes, making it crucial to control misclassification rates in the most critical and, in many realworld problems, minority cases, possibly at the expense of higher misclassification rates in less problematic classes. One traditional approach to address this problem consists of assigning misclassification costs to the different classes and applying the Bayes rule, by optimizing a loss function. However, fixing precise values for such misclassification costs may be problematic in realworld applications. In this paper we address the issue of misclassification for the Naïve Bayes classifier. Instead of requesting precise values of misclassification costs, threshold values are used for different performance measures. This is done by adding constraints to the optimization problem underlying the estimation process. Our findings show that, under a reasonable computational cost, indeed, the performance measures under consideration achieve the desired levels yielding a user-friendly constrained classification procedure. [ABSTRACT FROM AUTHOR]

This paper investigates how the production policy, as well as other factors, affect the facility location-allocation decisions. We focus on a p-median location problem in which one single perishable product is to be produced and shipped to a set of users. The time-correlated demands of the clients are generated by autoregressive processes, and they are forecasted from historical data. Empirically, we show that: (i) embedding the production policy in the location-allocation decision problem may lead to a facilities-clients assignment which does not necessarily correspond to the minimum cost allocation, but produces better profits, (ii) taking into account the autocorrelation of the demand can significantly improve the performance of the supply chain, and (iii) the variability of the demand strongly affects the performance of the supply chain, so a careful choice of production strategy is especially recommended in this case. [ABSTRACT FROM AUTHOR]

The capability of modeling non-exponentially distributed and dependent inter-arrival times as well as correlated batches makes the Batch Markovian Arrival Processes ( BMAP) suitable in different real-life settings as teletraffic, queueing theory or actuarial contexts. An issue to be taken into account for estimation purposes is the identifiability of the process. This paper explores the identifiability of the stationary two-state BMAP noted as BMAP ( k), where k is the maximum batch arrival size, under the assumptions that both the interarrival times and batches sizes are observed. It is proven that for k ≥ 2 the process cannot be identified. The proof is based on the construction of an equivalent BMAP( k) to a given one, and on the decomposition of a BMAP ( k) into k BMAP (2)s. [ABSTRACT FROM AUTHOR]

In this paper, we study how the lack of a unique representation for the two-state Markovian arrival process ( MAP) can influence statistical estimation for the MAP/ G/1 queueing system. In particular, given two equivalent representations of the same MAP, we find that the steady-state distributions of the queuing system can be identified. [ABSTRACT FROM AUTHOR]

In Ramírez-Cobo et al. (J Appl Probab 47(3):630-649, ), weakly equivalent second order Markovian arrival processes (noted MAPs) are introduced and partially characterized. In this work we look into weak equivalence in detail and provide a complete characterization of weakly equivalent MAPs. The analogous problem for the MAP case is partially solved. [ABSTRACT FROM AUTHOR]