Markov zero-inflated Poisson regression models for a time series of counts with excess zeros

2001 ◽  
Vol 28 (5) ◽  
pp. 623-632 ◽  
Author(s):  
Peiming Wang
Keyword(s):  
Time Series ◽  
Poisson Regression ◽  
Regression Models ◽  
Time Series Of Counts ◽  
Excess Zeros

scholarly journals A New Bivariate Random Coefficient INAR(1) Model with Applications

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 39
Author(s):  
Qi Li ◽  
Huaping Chen ◽  
Xiufang Liu
Keyword(s):  
Time Series ◽  
Random Coefficient ◽  
Finite Sample ◽  
Time Series Of Counts ◽  
New Model ◽  
Excess Zeros ◽  
Proposed Model ◽  
Over Dispersion ◽  
Conditional Maximum ◽  
Stochastic Properties

Excess zeros is a common phenomenon in time series of counts, but it is not well studied in asymmetrically structured bivariate cases. To fill this gap, we first considered a new first-order, bivariate, random coefficient, integer-valued autoregressive model with a bivariate innovation, which follows the asymmetric Hermite distuibution with five parameters. An attractive advantage of the new model is that the dependence between series is achieved by innovative parts and the cross-dependence of the series. In addition, the time series of counts are modeled with excess zeros, low counts and low over-dispersion. Next, we established the stationarity and ergodicity of the new model and found its stochastic properties. We discuss the conditional maximum likelihood (CML) estimate and its asymptotic property. We assessed finite sample performances of estimators through a simulation study. Finally, we demonstrate the superiority of the proposed model by analyzing an artificial dataset and a real dataset.

Statistical Inference for Zero-Inflated Poisson Regression Models with Excess Zeros

2013 ◽  
Vol 367 ◽  
pp. 253-258
Author(s):  
Lin Dai ◽  
Ying Zi Fu
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
Test Procedure ◽  
Score Test ◽  
Zero Inflation ◽  
Test Statistic ◽  
Inference Procedure ◽  
Data Set ◽  
Excess Zeros ◽  
Fire Accident

In this paper, we deal with a class of zero-inflated Poisson regression models and propose a score test procedure for assessing whether there exists zero-inflation or not. The sampling distribution and the power of the score test statistic are investigated by a limited simulation study. Furthermore, a Bayesian inference procedure is also presented for comparison. Finally, a data set of fire accident is used to illustrate our methodology and our numerical results show that our approach is useful and appealing for the analysis of count data with zero-inflation.

Limit theorems for regression models of time series of counts

2000 ◽  
Vol 46 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Michel Blais ◽  
Brenda MacGibbon ◽  
Roch Roy
Keyword(s):  
Time Series ◽  
Regression Models ◽  
Limit Theorems ◽  
Time Series Of Counts

Extreme Overdispersion and Persistence in Time-Series of Counts

2020 ◽  
Author(s):  
Leopoldo Catania ◽  
Eduardo Rossi ◽  
Paolo Santucci de Magistris
Keyword(s):  
Time Series ◽  
Time Series Of Counts
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