Bayesian inference for the negative binomial-generalized Lindley regression model: properties and applications

Aryuyuen, Sirinapa

doi:10.1080/03610926.2021.1995434

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…By replacing the mgf of  in (13) as in (15) with ( ), t r j = − + we have the pmf of Y as follows: ( )…”

Section: A New Mixed Negative Binomial Model For Time Series Count Datamentioning

confidence: 99%

“…For the parameter estimation method, some researchers were predisposed to the Bayesian approach over the MLE in recent times [19][20][21]. Some studies have used the Bayesian method to estimate parameters in the GLMs for Poisson and NB regression [13]. The practical advantages of the Bayesian approach are its flexibility and generality, as this allows it to cope with complex problems [21,22].…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Inference for the Negative Binomial-Quasi Lindley Model for Time Series Count Data on the COVID-19 Pandemic

Aryuyuen

Tonggumnead²

2022

Trends Sci

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In statistical models, the generalized linear model (GLMs) plays a role in studying to describe a response variable as a function of 1 or more predictor variables. Computational methods and mixed distributions are frequently used to build predictive models to perform time-to-event data analysis. To develop a statistical model so that the model can make predictions appropriately and accurately, it starts with developing a suitable distribution for the nature of the actual data. This paper proposes a new mixed negative binomial distribution for count data with over-dispersion, the so-called negative binomial-quasi Lindley (NB-QL) distribution. A new GLMs framework for the NB-QL model to build the time series count data model is introduced, and its application is carried out based on the actual data sets of the COVID-19 epidemic in Thailand. The models are related to GLMs as they are linear relationships between outcome variables and covariates. Where the response variable was in the form of time series count data under the exponential family distribution function, with the random components and link functions. In this study, we study the factors that affect the number of COVID-19 death cases in Thailand and provide the predictive modeling of the number of the COVID-19 death cases from 1 January 2020 to 31 December 2020, for which this data set has the observed sample of 366 days. In contrast, a model with an NB-QL distribution and NB has approached the uniform. Based on the deviance, DIC, and the probability integral transform histogram, we can see that the proposed model is also suitable for forecasting the number of the COVID-19 death cases daily in Thailand, indicating that the NB-QL time series model was another efficient alternative to modeling count data that has an over-dispersion problem. According to the NB-QL time series model about the number of the COVID-19 death cases daily in Thailand, it is indicated that the average number of daily COVID-19 deaths is influenced by the number of the COVID-19 death cases in the previous 3 days. The average number of COVID-19 death cases in Thailand is also influenced by the previous 2 days. At the same time, the number of infected cases daily in Thailand is influenced by the number of the COVID-19 death cases daily. In addition, there are also the components interventions of internal covariate effects due to the data, as there was a surge in the number of the COVID-19 death cases daily in Thailand at the time. HIGHLIGHTS A new mixture NB distribution to be a flexible alternative to analyze count data with over-dispersion. The new distribution is a mix of the NB and QL distributions; a name is Negative Binomial-Quasi Lindley A new mixed negative binomial distribution for time series count data with over-dispersion, and the Bayesian approach is the method used to estimate the parameters of the proposed model. We will apply the GLMs framework to build the time series count data The new mixed NB distribution in this study is an extremely effective alternative for modeling count data in the context of over-dispersion GRAPHICAL ABSTRACT

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“…By replacing the mgf of  in (13) as in (15) with ( ), t r j = − + we have the pmf of Y as follows: ( )…”

Section: A New Mixed Negative Binomial Model For Time Series Count Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Inference for the Negative Binomial-Quasi Lindley Model for Time Series Count Data on the COVID-19 Pandemic

Aryuyuen

Tonggumnead²

2022

Trends Sci

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Predicting crash occurrence at intersections in Texas: an opportunity for machine learning

Charm

Wang

Zuniga-Garcia

et al. 2023

Transportation Planning and Technology

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Bayesian Inference for a New Negative Binomial-Samade Model for Time Series Data Counts with Its Properties and Applications

Aryuyuen,

Thaimsorn,

Tonggumnead

2023

Wseas Transactions on Mathematics

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A new distribution was developed that mixed the negative binomial (NB) and Samade distributions, called the negative binomial-Samade (NB-SA) distribution. The properties of this distribution were studied, and the newly created distribution was applied using the framework of generalized linear models to build a time series data count model. The characteristics of overdispersion and heavy-tailed distribution of the count response variables were applied in the actual dataset modeling. Distribution parameters and the regression coefficient were estimated using a Bayesian approach. Results showed that the NB-SA model had significantly the highest efficiency compared with the classical NB and Poisson models for analyzing factors influencing the daily number of COVID-19 deaths in Thailand.

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Bayesian inference for the negative binomial-generalized Lindley regression model: properties and applications

Cited by 3 publications

References 33 publications

Bayesian Inference for the Negative Binomial-Quasi Lindley Model for Time Series Count Data on the COVID-19 Pandemic

Bayesian Inference for the Negative Binomial-Quasi Lindley Model for Time Series Count Data on the COVID-19 Pandemic

Predicting crash occurrence at intersections in Texas: an opportunity for machine learning

Bayesian Inference for a New Negative Binomial-Samade Model for Time Series Data Counts with Its Properties and Applications

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