Academic literature on the topic 'Poisson autoregression'

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Journal articles on the topic "Poisson autoregression"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Poisson autoregression"

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Sellner, Richard, Manfred M. Fischer, and Matthias Koch. "A Spatial Autoregressive Poisson Gravity Model." Wiley-Blackwell, 2013. http://dx.doi.org/10.1111/gean.12007.

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In this article, a Poisson gravity model is introduced that incorporates spatial dependence of the explained variable without relying on restrictive distributional assumptions of the underlying data-generating process. The model comprises a spatially filtered component including the origin-, destination-, and origin-destination-specific variables and a spatial residual variable that captures origin- and destination-based spatial autocorrelation. We derive a two-stage nonlinear least-squares (NLS) estimator (2NLS) that is heteroscedasticity- robust and, thus, controls for the problem of o
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Yontay, Petek. "A Two-sided Cusum For First-order Integer-valued Autoregressive Processes Of Poisson Counts." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613385/index.pdf.

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Count data are often encountered in manufacturing and service industries due to ease of data collection. These counts can be useful in process monitoring to detect shifts of a process from an in-control state to various out-of-control states. It is usually assumed that the observations are independent and identically distributed. However, in practice, observations may be autocorrelated and this may adversely affect the performance of the control charts developed under the assumption of independence. In this thesis, the cumulative sum (CUSUM) control chart for monitoring autocorrelated processe
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Glórias, Ludgero Miguel Carraça. "Estimating a knowledge production function and knowledge spillovers : a new two-step estimation procedure of a Spatial Autoregressive Poisson Model." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20711.

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Mestrado em Econometria Aplicada e Previsão<br>Vários estudos econométricos procuram explicar os determinantes da criação de conhecimento usando como variável dependente o número de patenteamentos numa região. Alguns destes procuram captar os efeitos de Knowledge Spillovers através de modelos lineares que incorporam dependência espacial. No entanto, nenhum estudo foi encontrado que captasse este efeito, tendo em atenção a natureza discreta da variável dependente. Este trabalho pretende preencher essa lacuna propondo um novo estimador de máxima verosimilhança a dois passos para um modelo Poisso
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Adolf, Janne K. "Contextualizing the Dynamics of Affective Functioning: Conceptual and Statistical Considerations." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19412.

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Aktuelle Affektforschung betont die Bedeutung mikrolängsschnittlicher Daten für das Verstehen täglichen affektiven Funktionierens, da sie es erlauben affektive Dynamiken und potentiell zugrunde liegende Prozesse zu beschreiben. Dynamische Längsschnittmodelle werden entsprechend attraktiver. In dieser Dissertation komme ich Forderungen nach einer Integration kontextueller Informationen in die Untersuchung täglichen affektiven Funktionierens nach. Speziell modifiziere ich populäre dynamische Modelle so, dass sie kontextuelle Variationen einbeziehen. In einem ersten Beitrag werden Personen als in
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Kratz, Marie. "Some contributions in probability and statistics of extremes." Habilitation à diriger des recherches, Université Panthéon-Sorbonne - Paris I, 2005. http://tel.archives-ouvertes.fr/tel-00239329.

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Constant, Camille. "Modélisation stochastique et analyse statistique de la pulsatilité en neuroendocrinologie." Thesis, Poitiers, 2019. http://www.theses.fr/2019POIT2330.

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L’objectif de cette thèse est de proposer plusieurs modèles probabilistes pour représenter l’activité calcique des neurones et comprendre son implication dans la sécrétion d’hormone GnRH. Ce travail s’appuie sur des expériences réalisées à l’INRA Centre Val-de-Loire. Le Chapitre 1 propose une modélisation continue, où nous étudions un processus markovien de type shot-noise. Le Chapitre 2 étudie un modèle discret de type AR(1) basé sur la discrétisation du modèle du Chapitre 1 et propose une première estimation des paramètres. Le Chapitre 3 propose un autre modèle discret de type AR(1) où les i
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Böhmová, Karolína. "Poissonovská autoregrese." Master's thesis, 2019. http://www.nusl.cz/ntk/nusl-397806.

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This thesis deals with INGARCH models for a count time series. Main emphasis is placed on a linear INARCH model. Its properties are derived. Several methods of estimation are introduced - maximum likelihood method, least squares method and its modifications - and later compared in a simulation study. Main properties and maximum likelihood estimation for INGARCH(1,1) model are stated. Higher order linear INGARCH models and nonlinear INGARCH models are discussed briefly. An application of the presented models on time series of car accidents is given.
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Chuang, Menghua, and 莊孟樺. "Solving Maximum Likelihood Estimators for Poisson Integer Autoregressive Model." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/29520351912439908326.

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碩士<br>東海大學<br>統計學系<br>100<br>On the traditional time series analysis, we deal with time series data which follows some normal distribution and so conventionally time series model ARMA or ARIMA are usually performed for analyzing the relation between the history and the future. In this study, we study time series of counts which are assumed to follow Poisson distribution marginally. We considered the Poisson Integer Autoregressive (INAR) proposed by Al-Osh, Alzaid(1987) and McKenzie(1988). For such model, RK Freeland and BPM McCabe (2004) derived likelihood functions as well as the score functi
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Baroud, Hiba. "Analysis of Financial Data using a Difference-Poisson Autoregressive Model." Thesis, 2011. http://hdl.handle.net/10012/5934.

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Box and Jenkins methodologies have massively contributed to the analysis of time series data. However, the assumptions used in these methods impose constraints on the type of the data. As a result, difficulties arise when we apply those tools to a more generalized type of data (e.g. count, categorical or integer-valued data) rather than the classical continuous or more specifically Gaussian type. Papers in the literature proposed alternate methods to model discrete-valued time series data, among these methods is Pegram's operator (1980). We use this operator to build an AR(p) model for intege
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HUANG, TING-SHUN, and 黃鼎舜. "Analysis and discussion for Poisson integer autoregressive time series model." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/87241664737097264646.

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碩士<br>東海大學<br>統計學系<br>99<br>Across different types of data, the choices of time series model is a key element to the time series analysis. The article first overview the poisson integer autoregressive model published by Alzaid, A. A. and Al-Osh, M.A. (1990) and referenced the paper issued by Cardinal M, Roy R, Lambert J. (1999) as the basis for analysis of both simulation data and actual data. In addition, the article will use a few simulation data, which correspond to the modeling condition of Poisson INAR. This will enable us to understand the feasibility of the model in some situations. Oth
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Book chapters on the topic "Poisson autoregression"

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Al-Wahsh, Huda, Sayed Ejaz Ahmed, and Abdulkadir Hussein. "A Bivariate Poisson Time Series of Counts with Two Latent Autoregressive Processes." In Proceedings of the Fifteenth International Conference on Management Science and Engineering Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79203-9_31.

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Ronning, Gerd, and Robert C. Jung. "Estimation of a First Order Autoregressive Process with Poisson Marginals for Count Data." In Advances in GLIM and Statistical Modelling. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_29.

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Ahmed, S. Ejaz, Khalifa Es-Sebaiy, Abdulkadir Hussein, Idir Ouassou, and Anne Snowdon. "An Efficient Estimation Strategy in Autoregressive Conditional Poisson Model with Applications to Hospital Emergency Department Data." In Contributions to Statistics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17519-1_12.

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Ari, Yakup. "Continuous Autoregressive Moving Average Models." In Methodologies and Applications of Computational Statistics for Machine Intelligence. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7701-1.ch007.

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The financial time series have a high frequency and the difference between their observations is not regular. Therefore, continuous models can be used instead of discrete-time series models. The purpose of this chapter is to define Lévy-driven continuous autoregressive moving average (CARMA) models and their applications. The CARMA model is an explicit solution to stochastic differential equations, and also, it is analogue to the discrete ARMA models. In order to form a basis for CARMA processes, the structures of discrete-time processes models are examined. Then stochastic differential equations, Lévy processes, compound Poisson processes, and variance gamma processes are defined. Finally, the parameter estimation of CARMA(2,1) is discussed as an example. The most common method for the parameter estimation of the CARMA process is the pseudo maximum likelihood estimation (PMLE) method by mapping the ARMA coefficients to the corresponding estimates of the CARMA coefficients. Furthermore, a simulation study and a real data application are given as examples.
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Conference papers on the topic "Poisson autoregression"

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Wang, Zheng, Prithwish Chakraborty, Sumiko R. Mekaru, John S. Brownstein, Jieping Ye, and Naren Ramakrishnan. "Dynamic Poisson Autoregression for Influenza-Like-Illness Case Count Prediction." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. http://dx.doi.org/10.1145/2783258.2783291.

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Nasution, Bahrul Ilmi, Yudhistira Nugraha, Juan Intan Kanggrawan, and Alex Lukmanto Suherman. "Forecasting of COVID-19 Cases in Jakarta using Poisson Autoregression." In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 2021. http://dx.doi.org/10.1109/icoict52021.2021.9527454.

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Mark, Ben, Garvesh Raskutti, and Rebecca Willett. "Network estimation via poisson autoregressive models." In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2017. http://dx.doi.org/10.1109/camsap.2017.8313192.

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Hall, Eric C., Garvesh Raskutti, and Rebecca Willett. "Inferring high-dimensional poisson autoregressive models." In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551762.

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Rohimah, Siti Rohmah, Qorry Meidianingsih, Nabilah Ninda Nur Azizah, and Ahmad Syauqy Baihaqy. "Analysis of factors causing the number of poor people in DKI Jakarta using spatial autoregressive Poisson model." In THE 2ND SCIENCE AND MATHEMATICS INTERNATIONAL CONFERENCE (SMIC 2020): Transforming Research and Education of Science and Mathematics in the Digital Age. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0041857.

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