For a population with any given number of types, we construct a new multivariate Moran process with frequency-dependent selection and establish, analytically, a correspondence to equilibrium Lotka-Volterra phenomenology. This correspondence, on the one hand, allows us to infer the phenomenology of our Moran process based on much simpler Lokta-Volterra phenomenology, and on the other, allows us ...

Traditionally, the process capability index is developed assuming that the process output data are independent and follow normal distribution. However, in most environmental cases, the process data have more than one quality characteristic and exhibit property of autocorrelation. We propose two novel multivariate process capability indices for autocorrelated data, NMACp and NMACpm for the-nomin...

Abstract: Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be s...

The main purpose of this research is to implement a multivariate feedback adjustment proportional to the last deviation from the target, in the set of variables wandering around the target. To apply the controller equation it will be necessary to study the exponentially weighted moving average (EWMA) statistic, in order to determine the behavior of the target disturbances. To determine the fore...

1.1. Point Processses and Intensities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1. Stochastic Integrals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2. The Autoregressive Conditional Duration Model . . . . . . . . . . . . . . . . . . . . . . . 2 1.3. The Autoregressive Conditional Intensity Model . . . . . . . . . . . . . . . ...

One-class classification problems have attracted a great deal of attention from various disciplines. In the present study, we attempt to extend the scope of application of the one-class classification technique to statistical process control (SPC) problems. We propose new multivariate control charts that apply the effectiveness of one-class classification to improvement of Phase I and Phase II ...

In the present work we analyze the problem of adaptation and evolution of RNA virus populations, by defining the basic stochastic model as a multivariate branching process in close relation with the branching process advanced by Demetrius, Schuster and Sigmund (“Polynucleotide evolution and branching processes”, Bull. Math. Biol. 46 (1985) 239-262), in their study of polynucleotide evolution. W...

Gaussian process regression models or ‘emulators’ have become popular in the statistical analysis of deterministic computer models (simulators), in particular for computationally expensive models where the emulator is used as a fast surrogate. For models with multivariate output, common practice is to specify a separable covariance structure for the Gaussian process. Though computationally conv...

Process monitoring by use of multivariate projection methods has received increasing attention as it can reduce the monitoring problem for richly instrumented industrial processes with many correlated variables. This article discusses the monitoring and control of a continuously operating experimental blast furnace (EBF). A case study outlines the need for monitoring and control of the EBF and ...

Generally, an industrial product has more than one quality characteristic. In order to establish performance measures for evaluating the capability of a multivariate manufacturing process, several multivariate process capability indices have been developed based on the assumption of normality. Quality characteristics of many manufacturing processes in the chemical, pharmaceutical and electronic...