Adaptive log-linear zero-inflated generalized Poisson autoregressive model with applications to crime counts

2020 ◽  
Vol 14 (3) ◽  
pp. 1493-1515
Author(s):  
Xiaofei Xu ◽  
Ying Chen ◽  
Cathy W. S. Chen ◽  
Xiancheng Lin
Keyword(s):  
Autoregressive Model ◽  
Generalized Poisson ◽  
Crime Counts ◽  
Log Linear ◽  
Poisson Autoregressive Model

scholarly journals Burden of salmonellosis, campylobacteriosis and listeriosis: a time series analysis, Belgium, 2012 to 2020

2017 ◽  
Vol 22 (38) ◽  
Author(s):  
Charline Maertens de Noordhout ◽  
Brecht Devleesschauwer ◽  
Juanita A Haagsma ◽  
Arie H Havelaar ◽  
Sophie Bertrand ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Autoregressive Model ◽  
Correction Factors ◽  
Disability Adjusted Life Years ◽  
Uncertainty Interval ◽  
Food Borne ◽  
Disability Adjusted Life ◽  
Life Years ◽  
Poisson Autoregressive Model

Salmonellosis, campylobacteriosis and listeriosis are food-borne diseases. We estimated and forecasted the number of cases of these three diseases in Belgium from 2012 to 2020, and calculated the corresponding number of disability-adjusted life years (DALYs). The salmonellosis time series was fitted with a Bai and Perron two-breakpoint model, while a dynamic linear model was used for campylobacteriosis and a Poisson autoregressive model for listeriosis. The average monthly number of cases of salmonellosis was 264 (standard deviation (SD): 86) in 2012 and predicted to be 212 (SD: 87) in 2020; campylobacteriosis case numbers were 633 (SD: 81) and 1,081 (SD: 311); listeriosis case numbers were 5 (SD: 2) in 2012 and 6 (SD: 3) in 2014. After applying correction factors, the estimated DALYs for salmonellosis were 102 (95% uncertainty interval (UI): 8–376) in 2012 and predicted to be 82 (95% UI: 6–310) in 2020; campylobacteriosis DALYs were 1,019 (95% UI: 137–3,181) and 1,736 (95% UI: 178–5,874); listeriosis DALYs were 208 (95% UI: 192–226) in 2012 and 252 (95% UI: 200–307) in 2014. New actions are needed to reduce the risk of food-borne infection with Campylobacter spp. because campylobacteriosis incidence may almost double through 2020.

scholarly journals A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 77 ◽  
Author(s):  
Arianna Agosto ◽  
Paolo Giudici
Keyword(s):  
Health Economics ◽  
Statistical Model ◽  
Autoregressive Model ◽  
Trend Model ◽  
Poisson Autoregressive Model

We present a statistical model which can be employed to understand the contagion dynamics of the COVID-19, which can heavily impact health, economics and finance. The model is a Poisson autoregression of the daily new observed cases, and can reveal whether contagion has a trend, and where is each country on that trend. Model results are exemplified from some observed series.

Estimation and testing for a Poisson autoregressive model

Metrika ◽  
2009 ◽  
Vol 73 (2) ◽  
pp. 211-230 ◽  
Author(s):  
Fukang Zhu ◽  
Dehui Wang
Keyword(s):  
Autoregressive Model ◽  
Poisson Autoregressive Model

A Linear Poisson Autoregressive Model: The Poisson AR(p) Model

2001 ◽  
Vol 9 (2) ◽  
pp. 164-184 ◽  
Author(s):  
Patrick T. Brandt ◽  
John T. Williams
Keyword(s):  
Time Series ◽  
Political Science ◽  
Autoregressive Model ◽  
Alternative Model ◽  
Moving Average ◽  
Dynamic Processes ◽  
Average Model ◽  
Moving Average Model ◽  
Poisson Autoregressive Model ◽  
Weighted Moving Average

Time series of event counts are common in political science and other social science applications. Presently, there are few satisfactory methods for identifying the dynamics in such data and accounting for the dynamic processes in event counts regression. We address this issue by building on earlier work for persistent event counts in the Poisson exponentially weighted moving-average model (PEWMA) of Brandt et al. (American Journal of Political Science44(4):823–843, 2000). We develop an alternative model for stationary mean reverting data, the Poisson autoregressive model of orderp, or PAR(p) model. Issues of identification and model selection are also considered. We then evaluate the properties of this model and present both Monte Carlo evidence and applications to illustrate.

scholarly journals Mean-parametrized Conway–Maxwell–Poisson regression models for dispersed counts

2017 ◽  
Vol 17 (6) ◽  
pp. 359-380 ◽  
Author(s):  
Alan Huang
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
Negative Binomial ◽  
Asymptotic Properties ◽  
Poisson Model ◽  
Finite Sample ◽  
Generalized Poisson ◽  
The Mean ◽  
Generalized Poisson Regression ◽  
Log Linear

Conway–Maxwell–Poisson (CMP) distributions are flexible generalizations of the Poisson distribution for modelling overdispersed or underdispersed counts. The main hindrance to their wider use in practice seems to be the inability to directly model the mean of counts, making them not compatible with nor comparable to competing count regression models, such as the log-linear Poisson, negative-binomial or generalized Poisson regression models. This note illustrates how CMP distributions can be parametrized via the mean, so that simpler and more easily interpretable mean-models can be used, such as a log-linear model. Other link functions are also available, of course. In addition to establishing attractive theoretical and asymptotic properties of the proposed model, its good finite-sample performance is exhibited through various examples and a simulation study based on real datasets. Moreover, the MATLAB routine to fit the model to data is demonstrated to be up to an order of magnitude faster than the current software to fit standard CMP models, and over two orders of magnitude faster than the recently proposed hyper-Poisson model.

scholarly journals Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Kassim Tawiah ◽  
Wahab Abdul Iddrisu ◽  
Killian Asampana Asosega
Keyword(s):  
Time Series ◽  
Autoregressive Model ◽  
Negative Binomial ◽  
Time Series Data ◽  
Partial Likelihood ◽  
Series Data ◽  
Number Of Zeros ◽  
Time Series Modelling ◽  
Poisson Autoregressive Model ◽  
Excessive Number

Discrete count time series data with an excessive number of zeros have warranted the development of zero-inflated time series models to incorporate the inflation of zeros and the overdispersion that comes with it. In this paper, we investigated the characteristics of the trend of daily count of COVID-19 deaths in Ghana using zero-inflated models. We envisaged that the trend of COVID-19 deaths per day in Ghana portrays a general increase from the onset of the pandemic in the country to about day 160 after which there is a general decrease onward. We fitted a zero-inflated Poisson autoregressive model and zero-inflated negative binomial autoregressive model to the data in the partial-likelihood framework. The zero-inflated negative binomial autoregressive model outperformed the zero-inflated Poisson autoregressive model. On the other hand, the dynamic zero-inflated Poisson autoregressive model performed better than the dynamic negative binomial autoregressive model. The predicted new death based on the zero-inflated negative binomial autoregressive model indicated that Ghana’s COVID-19 death per day will rise sharply few days after 30th November 2020 and drastically fall just as in the observed data.

Sign in / Sign up

Export Citation Format

Share Document