We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an autoregressive (AR) model and suggest using an information criterion (AIC o...

In this paper we propose two techniques for the estimation of the fundamental frequency of speech signals. The rst technique is based on the Autoregressive Harmonic Excitation (ARHE) speech model. ARHE model consists of an autoregressive process driven simultaneously by white noise and a periodic excitation. The second technique is based on the estimation of a complex sinusoid in white Gaussian...

Software reliability models play a dominant role in the analysis of failure data for real time command and control software systems. Goel and Okumoto model is a non homogenous Poisson Process software reliability growth model which has gained a lot of importance in software reliability analysis and prediction. The process of parameter estimation is the major drawback of this model because the i...

We examine the tail behaviour and extremal cluster characteristics of two-state Markov-switching autoregressive models where the first regime behaves like a random walk, the second regime is a stationary autoregression, and the generating noise is light-tailed. Under additional technical conditions we prove that the stationary solution has asymptotically exponential tail and the extremal index ...

In this paper variable bit rate VBR Moving Picture Experts Group (MPEG) coded full-motion video traffic is modeled by a nonlinear time-series process. The threshold autoregressive (TAR) process is of particular interest. The TAR model is comprised of a set of autoregressive (AR) processes that are switched between amplitude sub-regions. To model the dynamics of the switching between the sub-reg...

Parameter estimation of time-varying non-Gaussian autoregressive processes can be a highly nonlinear problem. The problem gets even more difficult if the functional form of the time variation of the process parameters is unknown. In this paper, we address parameter estimation of such processes by particle filtering, where posterior densities are approximated by sets of samples (particles) and p...