On semi-markov compartmental models with branching particles under the influence of disasters

A semi-Markov multi-compart mental system in which particles reproduce similar particles as a Markov branching process and being subjected to disasters is studied. Expressions for the mean number of particles alive at time t in each compartment are obtained. The results concerning irreversible, mammillarian and catenary compartmental systems have been discussed.     

Stochastic compartmental mooeld with branching particles and disasters: sojourn time and related characteristics

A semi-Markov cornpartmental model with branching particies is considered. The notion of disaster is incorporated into the structure. The means of (i) the total sojourn time, (ii) the number of deaths, (iii)the number of births and (iv)the number of emigrant particles in the system are analysed. Some interesting relations connecting these means are established. A few special cases are discussed in detail.     

Stochastic modeling of particle movement with application to marine biology and oceanography

We consider some stochastic models that have been proposed for the trajectories of moving objects, including Brownian motion. This leads to the development of a general approach for dealing with paths including the use of functional stochastic differential equations. We then present an empirical example based on the surface drifting movements of a small satellite-linked radio transmitter tag after it detached from a whale shark in the western Indian Ocean. The daily estimates of the tag’s locations were determined from transmissions received at irregular times by polar-orbiting satellites of the Argos Data Collection and Location Service system.     

Convolution of Binomial and Negative Binomial Variables

This paper considers a distribution formed by convolution of binomial and negative binomial variables. The distribution has the flexibility to adapt to the model under, equi, and over dispersion. Some properties of the proposed distribution are discussed, including characterization. Three stochastic processes leading to the distribution are also considered: (1) a three-dimensional random walk; (2) a birth, death, and immigration process; and (3) a thinned stochastic process.     

Stochastic volatility in mean models with scale mixtures of normal distributions and correlated errors: A Bayesian approach

A stochastic volatility in mean model with correlated errors using the symmetrical class of scale mixtures of normal distributions is introduced in this article. The scale mixture of normal distributions is an attractive class of symmetric distributions that includes the normal, Student-t, slash and contaminated normal distributions as special cases, providing a robust alternative to estimation in stochastic volatility in mean models in the absence of normality. Using a Bayesian paradigm, an efficient method based on Markov chain Monte Carlo (MCMC) is developed for parameter estimation. The methods developed are applied to analyze daily stock return data from the São Paulo Stock, Mercantile & Futures Exchange index (IBOVESPA). The Bayesian predictive information criteria (BPIC) and the logarithm of the marginal likelihood are used as model selection criteria. The results reveal that the stochastic volatility in mean model with correlated errors and slash distribution provides a significant improvement in model fit for the IBOVESPA data over the usual normal model.