A first‐order autoregressive process with one‐dimensional inverse Gaussian marginals is
introduced. The innovation distributions are obtained in certain special cases. The unknown …

In this article it is proved that the stationary Markov sequences generated by minification
models are ergodic and uniformly mixing. These results are used to establish the optimal …

This paper proposes different estimators for the parameters of SemiPareto and Pareto
autoregressive minification processes The asymptotic properties of the estimators are …

We investigate the stationarity of minification processes when the marginal is a discrete
distribution. There is a close relationship between the problem considered by Arnold and …

This paper deals with estimation of parameters and the mean life of a mixed failure time
distribution that has a discrete probability mass at zero and an exponential distribution with …

A common way to model nonnegative time series is to apply a log transformation and then
use classical ARMA techniques. We demonstrate using Canadian Fire Weather Index (FWI) …

The Markov sequences of non-negative random variables play important role in modelling
the time to events and time series. This chapter provides a detailed analysis of models …

In this chapter we discuss briefly univariate time series analysis with Autoregressive
Integrated Moving Average (ARIMA) models. We consider the three-stage model building …

In this paper we discuss briefly univariate time series analysis with Autoregressive
Integrated Moving Average (ARIMA) models. We consider the three stage model building …

We discuss stationary AR and ARMA time series models for sequences of integer-valued
random variables and continuous random variables. Stationary distribution of these models …