The INteger-valued AutoRegressive (INAR) processes were introduced in the literature by Al-Osh and Alzaid [1987. First-order integer-valued autoregressive (INAR(1)) process. J. Time Ser. Anal. 8, 261-275] and McKenzie [1988. Some ARMA models for dependent sequences of Poisson counts. Adv. Appl. Probab. 20, 822-835] for modelling correlated series of counts. These processes have been considered as the discrete counter part of AR processes, but their highly nonlinear characteristics lead to some statistically challenging problems, namely in parameter estimation. Several estimation procedures have been proposed in the literature, mainly for processes of first order. For some of these estimators the asymptotic properties as well as finite sampl...
The most commonly used method for estimating the time domain parameters of an autoregressive process...
Modelling counts of events can be found in several situations of real life. For instance, the number...
AbstractAutoregressive models are important in describing the behaviour of the observed time series....
The INteger-valued AutoRegressive (INAR) processes were introduced in the lite-rature by Al-Osh and ...
Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integervalu...
Abstract. Recently, as a result of the growing interest in modelling stationary processes with discr...
Integer-valued autoregressive (INAR) processes have been introduced to model non-negative integer-va...
This paper presents a modification and, at the same time, a generalization of the linear first order...
This paper presents a modification and, at the same time, a generalization of the linear first order...
Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integer-val...
Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integer-val...
AbstractIn this paper, we discuss integer-valued autoregressive time series (INAR), Hawkes point pro...
Integer-valued autoregressive (INAR) time series form a very useful class of processes suitable to m...
This paper is concerned with an integer-valued random walk process with qth-order autocorrelation. S...
The most commonly used method for estimating the time domain parameters of an autoregressive process...
The most commonly used method for estimating the time domain parameters of an autoregressive process...
Modelling counts of events can be found in several situations of real life. For instance, the number...
AbstractAutoregressive models are important in describing the behaviour of the observed time series....
The INteger-valued AutoRegressive (INAR) processes were introduced in the lite-rature by Al-Osh and ...
Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integervalu...
Abstract. Recently, as a result of the growing interest in modelling stationary processes with discr...
Integer-valued autoregressive (INAR) processes have been introduced to model non-negative integer-va...
This paper presents a modification and, at the same time, a generalization of the linear first order...
This paper presents a modification and, at the same time, a generalization of the linear first order...
Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integer-val...
Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integer-val...
AbstractIn this paper, we discuss integer-valued autoregressive time series (INAR), Hawkes point pro...
Integer-valued autoregressive (INAR) time series form a very useful class of processes suitable to m...
This paper is concerned with an integer-valued random walk process with qth-order autocorrelation. S...
The most commonly used method for estimating the time domain parameters of an autoregressive process...
The most commonly used method for estimating the time domain parameters of an autoregressive process...
Modelling counts of events can be found in several situations of real life. For instance, the number...
AbstractAutoregressive models are important in describing the behaviour of the observed time series....