We introduce a class of models for time series of counts which include INGARCH-type models as well as log linear models for conditionally Poisson distributed data. For those processes, we formulate simple conditions for stationarity and weak dependence with a geometric rate. The coupling argument used in the proof serves as a role model for a similar treatment of integer-valued time series models based on other types of thinning operations
A large variety of time series observation-driven models for binary and count data are currently use...
We are studying linear and log-linear models for multivariate count time series data with Poisson ma...
A random coefficient autoregressive process for count data based on a generalized thinning operator ...
We consider generalized linear models for regression modeling of count time series. We give easily v...
We propose a general definition for weak dependence of point processes as an alternative to mixing d...
International audienceWe propose a general definition for weak dependence of point processes as an a...
The aim of this paper is to develop a probabilistic study of a wide class of conditionally heterosce...
We prove a general functional central limit theorem for weak dependent time series. Those probabilis...
We investigate the relationship between weak dependence and mixing for discrete valued processes. We...
International audienceThe paper is devoted to recall weak dependence conditions from Dedecker et al....
Times series are main topics in modern statistical mathematics. They are essential for applications ...
International audienceThis monograph is aimed at developing Doukhan/Louhichi's (1999) idea to measur...
Starting from the compound Poisson INGARCH models, we introduce in this paper a new family of intege...
This paper is concerned with a general class of observation driven models for time series of counts ...
Functional data often arise from measurements on fine time grids and are obtained by separating an a...
A large variety of time series observation-driven models for binary and count data are currently use...
We are studying linear and log-linear models for multivariate count time series data with Poisson ma...
A random coefficient autoregressive process for count data based on a generalized thinning operator ...
We consider generalized linear models for regression modeling of count time series. We give easily v...
We propose a general definition for weak dependence of point processes as an alternative to mixing d...
International audienceWe propose a general definition for weak dependence of point processes as an a...
The aim of this paper is to develop a probabilistic study of a wide class of conditionally heterosce...
We prove a general functional central limit theorem for weak dependent time series. Those probabilis...
We investigate the relationship between weak dependence and mixing for discrete valued processes. We...
International audienceThe paper is devoted to recall weak dependence conditions from Dedecker et al....
Times series are main topics in modern statistical mathematics. They are essential for applications ...
International audienceThis monograph is aimed at developing Doukhan/Louhichi's (1999) idea to measur...
Starting from the compound Poisson INGARCH models, we introduce in this paper a new family of intege...
This paper is concerned with a general class of observation driven models for time series of counts ...
Functional data often arise from measurements on fine time grids and are obtained by separating an a...
A large variety of time series observation-driven models for binary and count data are currently use...
We are studying linear and log-linear models for multivariate count time series data with Poisson ma...
A random coefficient autoregressive process for count data based on a generalized thinning operator ...