Results 11 to 20 of about 174,276,385 (305)
Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers. [PDF]
PLoS ONE, 2013 INTRODUCTION: With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions' impact, approximations by ...
Olivier J T Briët +2 more
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Modelling time series of counts with overdispersion
Statistical Methods and Applications, 2008 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christian Weiß
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Truncated Poisson Regression for Time Series of Counts
Scandinavian Journal of Statistics, 2001 We consider partial likelihood analysis of a truncated Poisson regression model for time series of counts. We focus our attention on the study of asymptotic theory for the maximum partial likelihood estimator of a vector of regression parameters. Simulations and data analysis integrate the presentation.
Konstantinos Fokianos, Fokianos, K.
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The glarma Package for Observation-Driven Time Series Regression of Counts [PDF]
Journal of Statistical Software, 2015 We review the theory and application of generalized linear autoregressive moving average observation-driven models for time series of counts with explanatory variables and describe the estimation of these models using the R package glarma.
William T. M. Dunsmuir, David J. Scott
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Bayesian dynamic modeling of time series of dengue disease case counts.
PLoS Neglected Tropical Diseases, 2017 The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period ...
Daniel Adyro Martínez-Bello +2 more
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Journal of Time Series Analysis, 2022 Count time series are widely encountered in practice. As with continuous valued data, many count series have seasonal properties. This article uses a recent advance in stationary count time series to develop a general seasonal count time series modeling paradigm.
Jiajie Kong, Robert Lund
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Low-Count Time Series Anomaly Detection
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023 Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types. Several distinct challenges surface when modelling low-count time series, particularly low signal-to-noise ratios (when anomaly signatures are provably undetectable), and non-uniform performance (
Philipp Renz +4 more
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Bayesian tensor factorisations for time series of counts
Machine Learning, 2023 AbstractWe propose a flexible nonparametric Bayesian modelling framework for multivariate time series of count data based on tensor factorisations. Our models can be viewed as infinite state space Markov chains of known maximal order with non-linear serial dependence through the introduction of appropriate latent variables.
Zhongzhen Wang +2 more
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Poisson QMLE of Count Time Series Models [PDF]
Journal of Time Series Analysis, 2015 Regularity conditions are given for the consistency of the Poisson quasi‐maximum likelihood estimator of the conditional mean parameter of a count time series model. The asymptotic distribution of the estimator is studied when the parameter belongs to the interior of the parameter space and when it lies at the boundary.
Ahmad, Ali, Francq, Christian
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Prior Sensitivity Analysis in a Semi-Parametric Integer-Valued Time Series Model
Entropy, 2020 We examine issues of prior sensitivity in a semi-parametric hierarchical extension of the INAR(p) model with innovation rates clustered according to a Pitman−Yor process placed at the top of the model hierarchy.
Helton Graziadei +4 more
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