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Generalized Linear Models for count time series
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017In this paper we discuss a class of models for time series of low count data based on the Generalized Linear Model (GLM) approach. Unlike the traditional Auto-Regressive Moving-Average (ARMA) models for continuous Gaussian data, these models capture both the temporal correlation structure and the discrete marginal distribution of count data.
Nicholas Bosowski +2 more
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Bayesian methods for time series of count data
Communications in Statistics - Simulation and Computation, 2019In this paper, we consider Bayesian methods for analyzing time series of count data under a Poisson regression model with a latent auto-regressive process embedded as an additive error term.
Mohammed Obeidat +3 more
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A bilinear modeling in counts time series with applications
Communications in Nonlinear Science and Numerical SimulationzbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rana Bamdadi +2 more
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Bayesian Predictive Inference for Time Series Count Data
Biometrics, 2000Summary.Correlated count data arise often in practice, especially in repeated measures situations or instances in which observations are collected over time. In this paper, we consider a parametric model for a time series of counts by constructing a likelihood‐based version of a model similar to that of Zeger (1988,Biometrika75, 621–629). The model has
Chen, Ming-Hui, Ibrahim, Joseph G.
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Time series of count data: modeling, estimation and diagnostics
Computational Statistics & Data Analysis, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Robert C. Jung +2 more
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Semiparametric Regression for Time Series of Counts
Communications in Statistics - Theory and Methods, 2015We study estimation in a parameter-driven semiparametric regression model for time series of counts, where serial dependence among the observed counts is introduced by an autocorrelated latent process {ϵt}. The conditional mean ut of the response variable given {ϵt} is of the form , where Xt and Zt are covariates at time t, is an unknown parameter ...
Qin Wang, Rongning Wu
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A Time Weighted Neighbourhood Counting Similarity for Time Series Analysis
2008Time series data abound and analysis of such data is challenging and potentially rewarding. One example is financial time series analysis. In time series analysis there is the issue of time dependency, that is, the state in the nearer past is more relevant to the current state than that in the more distant past.
Wang, Hui, Lin, Zhiwei
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On Edgeworth models for count time series
Statistics & Probability Letters, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Count Time Series: A Methodological Review
Journal of the American Statistical Association, 2021Harry Joe, Vladas Pipiras
exaly