The Negative Binomial – Weighted Garima Distribution: Model, Properties and Applications

Bodhisuwan, Winai; Saengthong, Pornpop

doi:10.18187/pjsor.v16i1.3013

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Based on the mgf of the WG distribution (see Bodhisuwan & Saengthong (2020),   ( ) k X  can be written as ( 8).…”

Section: ) the Zero Truncated Negative Binomial-weighted Garima Distr...mentioning

confidence: 99%

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Zero Inflated and Zero Truncated Negative Binomial Weighted Garima Distributions for Modeling Count Data

Saengthong¹

2022

j.appsci

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In this paper, we introduced models for zero inflated and zero truncated based on the negative binomial-weighted Garima (NB-WG) distribution. The zero inflated negative binomialweighted Garima (ZINB-WG) distribution is a discrete probability distribution for the excessive zero counts and overdispersion which is a mixture of Bernoulli distribution and negative binomial-weighted Garima (NB-WG) distribution. Meanwhile, a new zero truncated distribution named as the zero truncated negative binomial-weighted Garima (ZTNB-WG) distribution can be used when the response variable is the set of positive integers. Some properties of the two different versions of NB-WG distribution are discussed and the estimation of the parameters is derived by maximum likelihood method (MLE). In addition, the usefulness of the proposed distributions is illustrated by real data sets.

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“…Based on the mgf of the WG distribution (see Bodhisuwan & Saengthong (2020),   ( ) k X  can be written as ( 8).…”

Section: ) the Zero Truncated Negative Binomial-weighted Garima Distr...mentioning

confidence: 99%

“…The negative binomial-weighted Garima (NB-WG) distribution is a new mixed NB distribution that includes three-parameters introduced by Bodhisuwan & Saengthong (2020). It represents generalization of distribution for count data, including the negative binomial-Garima (NB-G), and negative binomial-size biased Garima (NB-SBG).…”

Section: Introductionmentioning

confidence: 99%

Zero Inflated and Zero Truncated Negative Binomial Weighted Garima Distributions for Modeling Count Data

Saengthong¹

2022

j.appsci

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Bernoulli Poisson Moment Exponential Distribution: Mathematical Properties, Regression Model, and Applications

Alrumayh

2024

International Journal of Mathematics and Mathematical Sciences

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We introduce a new flexible count distribution by combining Bernoulli and Poisson moment exponential (PMEx) distributions. The new model named the Bernoulli PMEx distribution. Some mathematical properties are studied, including the hazard rate function, moments, moment generating function, probability generating function, and dispersion index. A count regression model is also proposed based on this distribution. The maximum likelihood estimation method is used to estimate the model parameters. In the end, three datasets from different fields are utilized for application purposes. The findings show that the new model efficiently analyzed these datasets as compared to Poisson, discrete Pareto, discrete Rayleigh, discrete Burr‐Hatke, and discrete inverted Topp–Leone distributions.

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Forecasting the South African labour market indicators: A comparison of ARIMA, count series models and machine learning regressors

Mogale,

Montshiwa,

Tsoku

2024

Preprint

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This paper compared count series, time series and machine learning models to determine the best data type (between count data and continuous data) and the best model for forecasting the labour market variables. Data from Statistics South Africa’s Quarterly Labour Force Survey (2008–2021) was used to compare ARIMA, Poisson autoregressive (PAR), negative binomial regression (NBR), generalised Poisson regression (GPR), support vector regression (SVR) and the multilayer perceptron (MLP) regressor based on the MAE, RMSE, MAPE, MSE and the plots of the actual values versus the forecasts from the models. The study showed that the most accurate approach to estimating the future values of the labour market variables is to implement PAR to forecast the counts of people who are unemployed, employed, and non-economically active, and then use these forecasts to compute the future values of unemployment rate, absorption rate and labour force participation rate.

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The Negative Binomial – Weighted Garima Distribution: Model, Properties and Applications

Cited by 3 publications

References 7 publications

Zero Inflated and Zero Truncated Negative Binomial Weighted Garima Distributions for Modeling Count Data

Zero Inflated and Zero Truncated Negative Binomial Weighted Garima Distributions for Modeling Count Data

Bernoulli Poisson Moment Exponential Distribution: Mathematical Properties, Regression Model, and Applications

Forecasting the South African labour market indicators: A comparison of ARIMA, count series models and machine learning regressors

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