Sentence examples for multivariate binomial process from inspiring English sources

Consider a multivariate binomial process with five correlated attributes.

Consider a multivariate binomial process with two correlated attributes, an in-control parameter vector [N', P 0 '] = [(20, 30)', (0.2, 0.15)'] and a correlation between the two attributes of 0.25.

In the 'Multivariate binomial process modeling and MLE derivation' section, the model of the process behavior is described and the MLEs of a step-change and a linear-trend disturbance are derived.

In this section, we assume that the real change type presented in a multivariate binomial process is a step change and derive the MLE of a change point in the process fraction non-conforming.

Monte Carlo simulations are used to evaluate and to compare the performances of the proposed change-point estimators designed for step changes and linear-trend disturbances, when the actual changes are of step changes and linear-trend disturbances in the process fraction non-conforming of a multivariate binomial process.

The transformation method was used to eliminate the skewness of multivariate binomial processes.

However, there is no research work concerning the change-point estimation of multivariate binomial processes, whereas in many situations, monitoring processes containing several quality characteristics of attribute type, each following a binomial distribution, is a need.

Everlasting interest in finding the better control charts in process monitoring is evident from Niaki and Khedmati (2013) who proposed a new control chart to monitor the change time of multivariate binomial processes for step changes and drifts.

Using Monte Carlo simulation experiments involving multivariate binomial processes with two and five attributes, the performances of the change-point estimators were evaluated by comparing them in the presence of real step-change and real linear-trend disturbances.

Assuming a linear-trend disturbance, in this section, the performances of the two proposed change-point estimators designed for step-change and linear-trend disturbances are evaluated and are compared in multivariate binomial processes with two and five correlated quality characteristics.

In particular, two multivariate beta and multivariate beta-binomial processes are discussed, and new distributions are introduced: the GMS-Sarmanov multivariate beta and its beta-binomial counterpart.