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Journal articles on the topic 'Poisson autoregression'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Fokianos, Konstantinos, Anders Rahbek, and Dag Tjøstheim. "Poisson Autoregression." Journal of the American Statistical Association 104, no. 488 (2009): 1430–39. http://dx.doi.org/10.1198/jasa.2009.tm08270.

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2

Fokianos, Konstantinos, and Dag Tjøstheim. "Nonlinear Poisson autoregression." Annals of the Institute of Statistical Mathematics 64, no. 6 (2012): 1205–25. http://dx.doi.org/10.1007/s10463-012-0351-3.

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3

Fokianos, Konstantinos, and Dag Tjøstheim. "Log-linear Poisson autoregression." Journal of Multivariate Analysis 102, no. 3 (2011): 563–78. http://dx.doi.org/10.1016/j.jmva.2010.11.002.

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4

Wang, Chao, Heng Liu, Jian-Feng Yao, Richard A. Davis, and Wai Keung Li. "Self-Excited Threshold Poisson Autoregression." Journal of the American Statistical Association 109, no. 506 (2014): 777–87. http://dx.doi.org/10.1080/01621459.2013.872994.

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5

Fokianos, Konstantinos, and Roland Fried. "Interventions in log-linear Poisson autoregression." Statistical Modelling: An International Journal 12, no. 4 (2012): 299–322. http://dx.doi.org/10.1177/1471082x1201200401.

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6

Brandt, Patrick T., and Todd Sandler. "A Bayesian Poisson Vector Autoregression Model." Political Analysis 20, no. 3 (2012): 292–315. http://dx.doi.org/10.1093/pan/mps001.

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Multivariate count models are rare in political science despite the presence of many count time series. This article develops a new Bayesian Poisson vector autoregression model that can characterize endogenous dynamic counts with no restrictions on the contemporaneous correlations. Impulse responses, decomposition of the forecast errors, and dynamic multiplier methods for the effects of exogenous covariate shocks are illustrated for the model. Two full illustrations of the model, its interpretations, and results are presented. The first example is a dynamic model that reanalyzes the patterns a

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7

Aknouche, Abdelhakim, and Nacer Demmouche. "Ergodicity conditions for a double mixed Poisson autoregression." Statistics & Probability Letters 147 (April 2019): 6–11. http://dx.doi.org/10.1016/j.spl.2018.11.030.

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8

Park, Taeyoung, and Seonghyun Jeong. "Analysis of Poisson varying-coefficient models with autoregression." Statistics 52, no. 1 (2017): 34–49. http://dx.doi.org/10.1080/02331888.2017.1353514.

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9

Escribano, Ana, and Mario Maggi. "Intersectoral default contagion: A multivariate Poisson autoregression analysis." Economic Modelling 82 (November 2019): 376–400. http://dx.doi.org/10.1016/j.econmod.2019.01.020.

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10

Basawa, I. V., and J. Zhou. "Non-Gaussian bifurcating models and quasi-likelihood estimation." Journal of Applied Probability 41, A (2004): 55–64. http://dx.doi.org/10.1017/s0021900200112203.

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A general class of Markovian non-Gaussian bifurcating models for cell lineage data is presented. Examples include bifurcating autoregression, random coefficient autoregression, bivariate exponential, bivariate gamma, and bivariate Poisson models. Quasi-likelihood estimation for the model parameters and large-sample properties of the estimates are discussed.

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11

Basawa, I. V., and J. Zhou. "Non-Gaussian bifurcating models and quasi-likelihood estimation." Journal of Applied Probability 41, A (2004): 55–64. http://dx.doi.org/10.1239/jap/1082552190.

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A general class of Markovian non-Gaussian bifurcating models for cell lineage data is presented. Examples include bifurcating autoregression, random coefficient autoregression, bivariate exponential, bivariate gamma, and bivariate Poisson models. Quasi-likelihood estimation for the model parameters and large-sample properties of the estimates are discussed.

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12

Prestemon, Jeffrey P., María L. Chas-Amil, Julia M. Touza, and Scott L. Goodrick. "Forecasting intentional wildfires using temporal and spatiotemporal autocorrelations." International Journal of Wildland Fire 21, no. 6 (2012): 743. http://dx.doi.org/10.1071/wf11049.

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We report daily time series models containing both temporal and spatiotemporal lags, which are applied to forecasting intentional wildfires in Galicia, Spain. Models are estimated independently for each of the 19 forest districts in Galicia using a 1999–2003 training dataset and evaluated out-of-sample with a 2004–06 dataset. Poisson autoregressive models of order P – PAR(P) models – significantly out-perform competing alternative models over both in-sample and out-of-sample datasets, reducing out-of-sample root-mean-squared errors by an average of 15%. PAR(P) and static Poisson models include

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13

Aknouche, Abdelhakim, Wissam Bentarzi, and Nacer Demouche. "On periodic ergodicity of a general periodic mixed Poisson autoregression." Statistics & Probability Letters 134 (March 2018): 15–21. http://dx.doi.org/10.1016/j.spl.2017.10.014.

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14

Freeland, R. K., and B. P. M. McCabe. "Analysis of low count time series data by poisson autoregression." Journal of Time Series Analysis 25, no. 5 (2004): 701–22. http://dx.doi.org/10.1111/j.1467-9892.2004.01885.x.

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15

Hudecová, Šárka, Marie Hušková, and Simos G. Meintanis. "Goodness–of–Fit Tests for Bivariate Time Series of Counts." Econometrics 9, no. 1 (2021): 10. http://dx.doi.org/10.3390/econometrics9010010.

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This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived and consistency is proved. The case of testing bivariate generalized Poisson autoregression and ext

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16

Agosto, Arianna, and Paolo Giudici. "A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics." Risks 8, no. 3 (2020): 77. http://dx.doi.org/10.3390/risks8030077.

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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.

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17

Zhu, Fukang, Shuangzhe Liu, and Lei Shi. "Local influence analysis for Poisson autoregression with an application to stock transaction data." Statistica Neerlandica 70, no. 1 (2015): 4–25. http://dx.doi.org/10.1111/stan.12071.

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18

Zeng, Qiang, Jiaren Sun, and Huiying Wen. "Bayesian Hierarchical Modeling Monthly Crash Counts on Freeway Segments with Temporal Correlation." Journal of Advanced Transportation 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/5391054.

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As the basis of traffic safety management, crash prediction models have long been a prominent focus in the field of freeway safety research. Studies usually take years or seasons as the observed time units, which may result in heterogeneity in crash frequency. To eliminate that heterogeneity, this study analyzes monthly crash counts and develops Bayesian hierarchical models with random effects, lag-1 autoregression (AR-1), and both (REAR-1) to accommodate the multilevel structure and temporal correlation in crash data. The candidate models are estimated and evaluated in the freeware WinBUGS us

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19

Hsu, Henda Y., and David McDowall. "Does Target-hardening Result in Deadlier Terrorist Attacks against Protected Targets? An Examination of Unintended Harmful Consequences." Journal of Research in Crime and Delinquency 54, no. 6 (2017): 930–57. http://dx.doi.org/10.1177/0022427817719309.

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Objectives: This study examines whether the use of target-hardening measures engenders greater amounts of casualty terrorist attacks against protected targets. Specifically, this study evaluates the impact of augmenting aviation security and protection of U.S. embassies and diplomats on the frequency and proportion of casualty attacks against aviation targets and U.S. diplomatic targets, respectively. Method: Using time-series data from the Global Terrorism Database (1970 to 2001), this study conducts time-series intervention analysis. To provide a more comprehensive test, a variety of supplem

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20

Doukhan, Paul, Konstantinos Fokianos, and Joseph Rynkiewicz. "Mixtures of Nonlinear Poisson Autoregressions." Journal of Time Series Analysis 42, no. 1 (2020): 107–35. http://dx.doi.org/10.1111/jtsa.12558.

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21

Brandt, Patrick T., and John T. Williams. "A Linear Poisson Autoregressive Model: The Poisson AR(p) Model." Political Analysis 9, no. 2 (2001): 164–84. http://dx.doi.org/10.1093/oxfordjournals.pan.a004869.

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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)

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22

Sellner, Richard, Manfred M. Fischer, and Matthias Koch. "A Spatial Autoregressive Poisson Gravity Model." Geographical Analysis 45, no. 2 (2013): 180–201. http://dx.doi.org/10.1111/gean.12007.

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23

Doukhan, Paul, Konstantinos Fokianos, and Dag Tjøstheim. "On weak dependence conditions for Poisson autoregressions." Statistics & Probability Letters 82, no. 5 (2012): 942–48. http://dx.doi.org/10.1016/j.spl.2012.01.015.

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24

Kang, Jiwon, and Sangyeol Lee. "Parameter Change Test for Poisson Autoregressive Models." Scandinavian Journal of Statistics 41, no. 4 (2014): 1136–52. http://dx.doi.org/10.1111/sjos.12088.

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25

Zhao, Zhiwen, Dehui Wang, and Cuixin Peng. "Conditional heteroscedasticity test for Poisson autoregressive model." Communications in Statistics - Theory and Methods 46, no. 9 (2016): 4437–48. http://dx.doi.org/10.1080/03610926.2015.1085560.

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26

Tawiah, Kassim, Wahab Abdul Iddrisu, and Killian Asampana Asosega. "Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana." Journal of Environmental and Public Health 2021 (April 30, 2021): 1–9. http://dx.doi.org/10.1155/2021/5543977.

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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

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27

Kitromilidou, Stella, and Konstantinos Fokianos. "Mallows’ quasi-likelihood estimation for log-linear Poisson autoregressions." Statistical Inference for Stochastic Processes 19, no. 3 (2015): 337–61. http://dx.doi.org/10.1007/s11203-015-9131-z.

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28

Agosto, Arianna, Giuseppe Cavaliere, Dennis Kristensen, and Anders Rahbek. "Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX)." Journal of Empirical Finance 38 (September 2016): 640–63. http://dx.doi.org/10.1016/j.jempfin.2016.02.007.

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29

Kang, Jiwon, and Junmo Song. "Robust parameter change test for Poisson autoregressive models." Statistics & Probability Letters 104 (September 2015): 14–21. http://dx.doi.org/10.1016/j.spl.2015.04.027.

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30

Zhu, Fukang, and Dehui Wang. "Estimation and testing for a Poisson autoregressive model." Metrika 73, no. 2 (2009): 211–30. http://dx.doi.org/10.1007/s00184-009-0274-z.

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31

Christou, Vasiliki, and Konstantinos Fokianos. "Estimation and testing linearity for non-linear mixed poisson autoregressions." Electronic Journal of Statistics 9, no. 1 (2015): 1357–77. http://dx.doi.org/10.1214/15-ejs1044.

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32

Ghahramani, M., and A. Thavaneswaran. "On some properties of Autoregressive Conditional Poisson (ACP) models." Economics Letters 105, no. 3 (2009): 273–75. http://dx.doi.org/10.1016/j.econlet.2009.08.018.

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33

Kang, Jiwon, and Junmo Song. "Score test for parameter change in Poisson autoregressive models." Economics Letters 160 (November 2017): 33–37. http://dx.doi.org/10.1016/j.econlet.2017.08.021.

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34

Kang, Jiwon, and Sangyeol Lee. "Minimum density power divergence estimator for Poisson autoregressive models." Computational Statistics & Data Analysis 80 (December 2014): 44–56. http://dx.doi.org/10.1016/j.csda.2014.06.009.

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35

Chen, Cathy W. S., and Sangyeol Lee. "Generalized Poisson autoregressive models for time series of counts." Computational Statistics & Data Analysis 99 (July 2016): 51–67. http://dx.doi.org/10.1016/j.csda.2016.01.009.

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36

Takalo, Reijo, Heli Hytti, Heimo Ihalainen, and Antti Sohlberg. "Edge-preserving adaptive autoregressive model for Poisson noise reduction." Nuclear Medicine Communications 42, no. 6 (2021): 707–10. http://dx.doi.org/10.1097/mnm.0000000000001377.

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37

Altun, Emrah. "A new one-parameter discrete distribution with associated regression and integer-valued autoregressive models." Mathematica Slovaca 70, no. 4 (2020): 979–94. http://dx.doi.org/10.1515/ms-2017-0407.

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AbstractThis study introduces the Poisson-Bilal distribution and its associated two models for modeling the over-dispersed count data sets. The Poisson-Bilal distribution has tractable properties and explicit forms for its statistical properties. A new over-dispersed count regression model and integer-valued autoregressive process with flexible innovation distribution are defined and studied comprehensively. Two real data sets are analyzed to prove empirically the importance of proposed models. Empirical findings show that the Poisson-Bilal distribution has important application fields in time

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38

Carvalho, Alexandre X., and Martin A. Tanner. "Modelling nonlinear count time series with local mixtures of Poisson autoregressions." Computational Statistics & Data Analysis 51, no. 11 (2007): 5266–94. http://dx.doi.org/10.1016/j.csda.2006.09.032.

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39

Lee, Sangyeol, Youngmi Lee, and Cathy W. S. Chen. "Parameter change test for zero-inflated generalized Poisson autoregressive models." Statistics 50, no. 3 (2015): 540–57. http://dx.doi.org/10.1080/02331888.2015.1083020.

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40

Alzaid, A. A., and M. A. Al-Osh. "Some autoregressive moving average processes with generalized Poisson marginal distributions." Annals of the Institute of Statistical Mathematics 45, no. 2 (1993): 223–32. http://dx.doi.org/10.1007/bf00775809.

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41

Kim, Byungsoo, Sangyeol Lee, and Dongwon Kim. "Robust Estimation for Bivariate Poisson INGARCH Models." Entropy 23, no. 3 (2021): 367. http://dx.doi.org/10.3390/e23030367.

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In the integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models, parameter estimation is conventionally based on the conditional maximum likelihood estimator (CMLE). However, because the CMLE is sensitive to outliers, we consider a robust estimation method for bivariate Poisson INGARCH models while using the minimum density power divergence estimator. We demonstrate the proposed estimator is consistent and asymptotically normal under certain regularity conditions. Monte Carlo simulations are conducted to evaluate the performance of the estimator in the presence of

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42

Takalo, R., H. Hytti, and H. Ihalainen. "Adaptive Autoregressive Model for Reduction of Poisson Noise in Scintigraphic Images." Journal of Nuclear Medicine Technology 39, no. 1 (2011): 19–26. http://dx.doi.org/10.2967/jnmt.110.077081.

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43

Kang, Jiwon, and Junmo Song. "A robust approach for testing parameter change in Poisson autoregressive models." Journal of the Korean Statistical Society 49, no. 4 (2020): 1285–302. http://dx.doi.org/10.1007/s42952-020-00056-7.

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44

Doukhan, Paul, and William Kengne. "Inference and testing for structural change in general Poisson autoregressive models." Electronic Journal of Statistics 9, no. 1 (2015): 1267–314. http://dx.doi.org/10.1214/15-ejs1038.

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45

Groß-KlußMann, Axel, and Nikolaus Hautsch. "Predicting Bid-Ask Spreads Using Long-Memory Autoregressive Conditional Poisson Models." Journal of Forecasting 32, no. 8 (2013): 724–42. http://dx.doi.org/10.1002/for.2267.

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46

Wang, Xinyang, Dehui Wang, and Haixiang Zhang. "Poisson autoregressive process modeling via the penalized conditional maximum likelihood procedure." Statistical Papers 61, no. 1 (2017): 245–60. http://dx.doi.org/10.1007/s00362-017-0938-0.

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47

Huang, Jie, and Fukang Zhu. "A New First-Order Integer-Valued Autoregressive Model with Bell Innovations." Entropy 23, no. 6 (2021): 713. http://dx.doi.org/10.3390/e23060713.

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A Poisson distribution is commonly used as the innovation distribution for integer-valued autoregressive models, but its mean is equal to its variance, which limits flexibility, so a flexible, one-parameter, infinitely divisible Bell distribution may be a good alternative. In addition, for a parameter with a small value, the Bell distribution approaches the Poisson distribution. In this paper, we introduce a new first-order, non-negative, integer-valued autoregressive model with Bell innovations based on the binomial thinning operator. Compared with other models, the new model is not only simp

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48

Mckenzie, Ed. "Some ARMA models for dependent sequences of poisson counts." Advances in Applied Probability 20, no. 04 (1988): 822–35. http://dx.doi.org/10.1017/s0001867800018395.

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A family of models for discrete-time processes with Poisson marginal distributions is developed and investigated. They have the same correlation structure as the linear ARMA processes. The joint distribution of n consecutive observations in such a process is derived and its properties discussed. In particular, time-reversibility and asymptotic behaviour are considered in detail. A vector autoregressive process is constructed and the behaviour of its components, which are Poisson ARMA processes, is considered. In particular, the two-dimensional case is discussed in detail.

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49

Mckenzie, Ed. "Some ARMA models for dependent sequences of poisson counts." Advances in Applied Probability 20, no. 4 (1988): 822–35. http://dx.doi.org/10.2307/1427362.

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A family of models for discrete-time processes with Poisson marginal distributions is developed and investigated. They have the same correlation structure as the linear ARMA processes. The joint distribution of n consecutive observations in such a process is derived and its properties discussed. In particular, time-reversibility and asymptotic behaviour are considered in detail. A vector autoregressive process is constructed and the behaviour of its components, which are Poisson ARMA processes, is considered. In particular, the two-dimensional case is discussed in detail.

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50

Kharin, Yuriy, and Maryna Zhurak. "Statistical Analysis of Spatio-Temporal Data Based on Poisson Conditional Autoregressive Model." Informatica 26, no. 1 (2015): 67–87. http://dx.doi.org/10.15388/informatica.2015.39.

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