A time series model based on dependent zero inflated counting series

Shamma, N.; Mohammadpour, Mehrnaz; Shirozhan, Masoumeh

doi:10.1007/s00180-020-00982-4

Cited by 12 publications

(5 citation statements)

References 20 publications

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“…Different variants of similar regression models have been proposed in the literature. A noteworthy use of copula for zero-inflated data is studied in Shamma et al (2020), where the inflation is built from a geometric count time series in an integer-valued autoregressive (INAR) process. Using the copula theory, the joint distribution function of Y t , Y t-1 can be written as below.…”

Section: The Zero-inflated Discrete Datamentioning

confidence: 99%

Review of Copula for Bivariate Distributions of Zero-Inflated Count Time Series Data

Fernando¹,

Alqawba²,

Samad³

et al. 2022

IJSP

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The class of bivariate integer-valued time series models, described via copula theory, is gaining popularity in the literature because of applications in health sciences, engineering, financial management and more. Each time series follows a Markov chain with the serial dependence captured using copula-based distribution functions from the Poisson and the zero-inflated Poisson margins. The copula theory is again used to capture the dependence between the two series. However, the efficiency and adaptability of the copula are being challenged because of the discrete nature of data and also in the case of zero-inflation of count time series. Likelihood-based inference is used to estimate the model parameters for simulated and real data with the bivariate integral of copula functions. While such copula functions offer great flexibility in capturing dependence, there remain challenges related to identifying the best copula type for a given application.  This paper presents a survey of the literature on bivariate copula for discrete data with an emphasis on the zero-inflated nature of the modelling. We demonstrate additional experiments on to confirm that the copula has potential as greater research area.

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Section: The Zero-inflated Discrete Datamentioning

confidence: 99%

Review of Copula for Bivariate Distributions of Zero-Inflated Count Time Series Data

Fernando¹,

Alqawba²,

Samad³

et al. 2022

IJSP

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“…First, we review the definition of the GNB thinning operator defined by Shamma et al. ( 2020 ), and then, an INAR(1) model with TRT-DBH innovation is constructed.…”

Section: Formulation Of the Inar(1) Model With Trt-dbh Innovationmentioning

confidence: 99%

“…(Shamma et al. 2020 ) Consider a sequence of independent identically distributed (iid) geometric random variables with parameter and Bernoulli random variable W with parameter , , where and W are independent for all . Define a sequence of dependent random variables as .…”

Section: Formulation Of the Inar(1) Model With Trt-dbh Innovationmentioning

confidence: 99%

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An INAR(1) Time Series Model via a Modified Discrete Burr–Hatke with Medical Applications

Shirozhan¹,

Khan

Bakouch

2022

Iran J Sci

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This paper introduces a flexible discrete transmuted record type discrete Burr–Hatke (TRT-DBH) model that seems suitable for handling over-dispersion and equi-dispersion in count data analysis. Further to the elegant properties of the TRT-DBH, we propose, in the time series context, a first-order integer-valued autoregressive process with TRT-DBH distributed innovations [TRBH-INAR(1)]. The moment properties and inferential procedures of this new INAR(1) process are studied. Some Monte Carlo simulation experiments are executed to assess the consistency of the parameters of the TRBH-INAR(1) model. To further motivate its purpose, the TRBH-INAR(1) is applied to analyze the series of the COVID-19 deaths in Netherlands and the series of infected cases due to the Tularaemia disease in Bavaria. The proposed TRBH-INAR(1) model yields superior fitting criteria than other established competitive INAR(1) models in the literature. Further diagnostics related to the residual analysis and forecasting based on the TRBH-INAR(1) model are also discussed. Based on modified Sieve bootstrap predictors, we provide integer forecasts of future death of COVID-19 and infected of Tularemia.

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“…Moreover, time series techniques are useful to assess the dynamical mechanisms of monthly measles incidence rate over years. Regarding the excess number of zero cases in the recent years, applying appropriate zero-in ated methods can help us to nd the most important factors resulting in non-occurrence of measles [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Measles Incidence Rates Using Machine Learning and Time Series Methods in the Center of Iran; 1997-2020

Nazari¹,

Fathi²,

Sharahi³

et al. 2020

Preprint

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Background: Measles is a feverish condition labeled among the most infectious viral illnesses in the globe. Despite the presence of a secure, accessible, affordable and efficient vaccine, measles continues to be a worldwide concern. Methods: This study uses machine learning and time series methods to assess factors that placed people at a higher risk of measles. This historical cohort study contained the Measles incidence in Markazi Province, the center of Iran, from April 1997 to February 2020. Logistic regression, linear discriminant analysis, random forest, artificial neural network, bagging, support vector machine, and naïve Bayes were used to make the classification. Zero-inflated negative binomial regression for time series was utilized to assess development of measles over time. Results: The prevalence of measles was 14.5% over the recent 24 years and a constant trend of almost zero cases was observed from 2002 to 2020. The order of independent variable importance were recent years, age, vaccination, rhinorrhea, male sex, contact with measles patients, cough, conjunctivitis, ethnic, and fever. Younger age, less probability of contact and no fever is associated with less odds of zero cases. Only 7 new cases were forecasted for the next two years. Bagging and random forest were the most accurate classification methods. Conclusion: Even if the numbers of new cases are almost zero during the recent years, it has been showed that age and contact are responsible for non-occurrence of measles. October and May are prone to have new cases for 2021 and 2022.

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A time series model based on dependent zero inflated counting series

Cited by 12 publications

References 20 publications

Review of Copula for Bivariate Distributions of Zero-Inflated Count Time Series Data

Review of Copula for Bivariate Distributions of Zero-Inflated Count Time Series Data

An INAR(1) Time Series Model via a Modified Discrete Burr–Hatke with Medical Applications

Evaluating Measles Incidence Rates Using Machine Learning and Time Series Methods in the Center of Iran; 1997-2020

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