Copula-based Markov zero-inflated count time series models with application

Alqawba, Mohammed; Diawara, Norou

doi:10.1080/02664763.2020.1748581

Cited by 10 publications

(13 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Maximizing the log-likelihood function in (7) provides ML estimates for the proposed class of models. However, within the log-likelihood function exists a bivariate normal or tintegral function that does not have a closed function as shown in (3). Hence, we evaluated the bivariate integral function using the standard randomized importance sampling method introduced by [20], which has been proven to be effective with dimensions less than ten.…”

Section: Estimation Methodsmentioning

confidence: 99%

“…. , n. Using copula theory, we can separately study {Y 1t } n t=1 and {Y 2t } n t=1 and their joint behavior, which would describe the cross-dependence among the bivariate series and with the assumption that each series {Y 1t } n t=1 and {Y 2t } n t=1 follows a copula-based Markov process (see [2,3,19] for examples).…”

Section: Constructing the Bivariate Modelsmentioning

confidence: 99%

“…Following a similar framework of building bivariate models for ordinal panel data via [1], we constructed a class of bivariate integer-valued time series models using copula theory. Applying either the bivariate Gaussian copula or the bivariate t-copula functions, we jointly modeled two copula-based Markov time series models (see [2,3]). Such a method allows for flexible dependence structures between the two time series and within each one, which allow for both positive or negative correlations, which is theoretically not presented with most existing methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Class of Copula-Based Bivariate Poisson Time Series Models with Applications

2021

Self Cite

View full text Add to dashboard Cite

A class of bivariate integer-valued time series models was constructed via copula theory. Each series follows a Markov chain with the serial dependence captured using copula-based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins. The copula theory was also used again to capture the dependence between the two series using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible dependence structure that allows for positive and negative correlation, as well. In addition, the use of a copula permits applying different margins with a complicated structure such as the ZIP distribution. Likelihood-based inference was used to estimate the models’ parameters with the bivariate integrals of the Gaussian or t-copula functions being evaluated using standard randomized Monte Carlo methods. To evaluate the proposed class of models, a comprehensive simulated study was conducted. Then, two sets of real-life examples were analyzed assuming the Poisson and the ZIP marginals, respectively. The results showed the superiority of the proposed class of models.

show abstract

Section: Estimation Methodsmentioning

confidence: 99%

Section: Constructing the Bivariate Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Class of Copula-Based Bivariate Poisson Time Series Models with Applications

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Joe 23 later compared the Markov models to other count time series models and explained the advantages of using them. Alqawba and Diawara 24 expanded the work on copula‐based Markov models by introducing a class of zero‐inflated models.…”

Section: Markov Models For Count Datamentioning

confidence: 99%

Residual‐based cumulative sum charts to monitor time series of counts via copula‐based Markov models

Alqawba

Kim

Radwan

2022

Appl Stoch Models Bus & Ind

Self Cite

View full text Add to dashboard Cite

Several scientific observations produce data that consist of serially dependent counts that are difficult to accurately analyze due to the absence of normality and the limited literature on dealing with such data. In this article, we propose a cumulative sum chart to monitor serially dependent counts using copula-based Markov models. After reviewing such models, we introduce the randomized quantile residuals obtained from the Markov process. The proposed method is evaluated using a comprehensive simulation study and a real-life example.

show abstract

“…More recently, Alqawba, Diawara, and Chaganty (2019) and Alqawba and Diawara (2020) proposed the use of copula‐based time series models for ZI counts in the presence of covariates. Their work considered the settings of ZIP, ZINB, and ZICMP distributed marginals.…”

Section: Zi Count Time Series Modelsmentioning

confidence: 99%

Zero‐inflatedmodeling partII:Zero‐inflatedmodels for complex data structures

Young

Roemmele

Shi

2020

WIREs Computational Stats

View full text Add to dashboard Cite

The prequel to this review provided an extensive treatment of classic zero‐inflated count regression models where a univariate discrete distribution is used for the count regression component of the model. The treatment of zero inflation beyond the classic univariate count regression setting has seen a substantial increase in recent years. This second review paper surveys some of this recent literature and focuses on important developments in handling zero inflation for correlated count settings, discrete time series models, spatial models, and multivariate models. We discuss some of the available computational tools for performing estimation in these settings, while again highlighting the diverse data problems that have been addressed using these methods. This article is categorized under: Statistical Models > Multivariate Models Statistical Models > Generalized Linear Models Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory

show abstract

Copula-based Markov zero-inflated count time series models with application

Cited by 10 publications

References 25 publications

A Class of Copula-Based Bivariate Poisson Time Series Models with Applications

A Class of Copula-Based Bivariate Poisson Time Series Models with Applications

Residual‐based cumulative sum charts to monitor time series of counts via copula‐based Markov models

Zero‐inflatedmodeling partII:Zero‐inflatedmodels for complex data structures

Contact Info

Product

Resources

About