Add to ORKGIn this paper we show that particular Gibbs sampler Markov processes can be modified to an autoregressive Markov process. The procedure allows the easy derivation of the innovation variables which provide strictly stationary autoregressive processes with fixed marginals. In particular, we provide the innovation variables for beta, gamma and Dirichlet processes.Autoregressive process Cadlag functions space Continuous time Markov process Discrete time Markov process Lévy process
The article of record as published may be found at http://dx.doi.org/10.2307/1426429It is shown that...
We introduce an autoregressive process called generalized normal-Laplace autoregressive process with...
We present a stochastic model which yields a stationary Markov process whose invariant distribution ...
In this paper we show that particular Gibbs sampler Markov processes can be modified to an autoregre...
This paper extends recent ideas for constructing classes of stationary autoregressive processes of o...
AbstractA general Markov process with innovation is introduced and its properties are studied. Based...
In this paper, we study the Gibbs sampler algorithm and explore some of its applications. First, we ...
International audienceDiscretization of continuous time autoregressive (AR) processes driven by a Br...
In this paper, we provide a method for modelling stationary time series. We allow the family of mar...
A non-stationary time series is one in which the statistics of the process are a function of time; t...
We extend the class of linear quantile autoregression models by allowing for the possibility of Mark...
SIGLEAvailable from British Library Document Supply Centre-DSC:3597.760(99/468) / BLDSC - British Li...
We examine autoregressive time series models that are subject to regime switching. These shifts are ...
Let us consider a continuous time Markov additive process with cadlag paths and a sequence of ran...
In this paper, we provide a new method for modelling stationary time series, concentrating on volati...
The article of record as published may be found at http://dx.doi.org/10.2307/1426429It is shown that...
We introduce an autoregressive process called generalized normal-Laplace autoregressive process with...
We present a stochastic model which yields a stationary Markov process whose invariant distribution ...
In this paper we show that particular Gibbs sampler Markov processes can be modified to an autoregre...
This paper extends recent ideas for constructing classes of stationary autoregressive processes of o...
AbstractA general Markov process with innovation is introduced and its properties are studied. Based...
In this paper, we study the Gibbs sampler algorithm and explore some of its applications. First, we ...
International audienceDiscretization of continuous time autoregressive (AR) processes driven by a Br...
In this paper, we provide a method for modelling stationary time series. We allow the family of mar...
A non-stationary time series is one in which the statistics of the process are a function of time; t...
We extend the class of linear quantile autoregression models by allowing for the possibility of Mark...
SIGLEAvailable from British Library Document Supply Centre-DSC:3597.760(99/468) / BLDSC - British Li...
We examine autoregressive time series models that are subject to regime switching. These shifts are ...
Let us consider a continuous time Markov additive process with cadlag paths and a sequence of ran...
In this paper, we provide a new method for modelling stationary time series, concentrating on volati...
The article of record as published may be found at http://dx.doi.org/10.2307/1426429It is shown that...
We introduce an autoregressive process called generalized normal-Laplace autoregressive process with...
We present a stochastic model which yields a stationary Markov process whose invariant distribution ...