In this paper, we introduce a new threshold model with poisson innovation: Threshold Integer-Valued Moving Average model (TINMA). We derive the numerical characteristics of TINMA(1) model. Stationary and ergodicity are also obtained. The methods of estimation under analysis is Yule-Walker. Some simulation results illustrate the performance of the proposed method.

In this paper it is shown that one can estimate the sum of the weights used to form a stationary moving average stochastic process based on nonnegative random variables by taking the limit in probability of suitable quotients, even when the random variables involved have infinite expectation.

A Bayesian model selection for modelling a time series by an autoregressive–moving–average model (ARMA) is presented. The posterior distribution of unknown parameters and the selected orders are obtained by the Markov chain Monte Carlo (MCMC) method. An MCMC algorithm that represents the parameters of the model as a point process has been implemented. The method is illustrated on simulated seri...

Often the least appropriate assumption in traditional control charting technology is that process data constitute a random sample. In reality most process data are correlated—either temporally, spatially, or due to nested sources of variation. One approach to monitoring temporally correlated data uses a control chart on the forecast errors from a time series model of the process with, possibly,...

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