A New Mixed Negative Binomial Distribution

Pudprommar, Chookait; Bodhisuwan, Winai; Zeephongse, Panlop

doi:10.3923/jas.2012.1853.1858

Cited by 21 publications

(11 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Count data with overdispersion and extra zeros are often solved by mixing the Poisson or NB distribution with a lifetime distribution. Many studies have shown that mixed and compound distributions such as the Poisson-inverse Gaussian distribution (Willmot, 1987), NB-inverse Gaussian distribution (Gómez-Déniz et al, 2008), NB-lindley distribution (Zamani and Ismail, 2010), NB-beta exponential distribution (Pudprommarat et al, 2012), NB-erlang distribution (Kongrod et al, 2014), and NB-Sushila distribution (Yamrubboon et al, 2017) are more suitable for the count data than the Poisson and NB distributions. Therefore, in order to obtain another competitive alternative mixed distribution to the aforementioned distributions, we looked for a new lifetime distribution that can be expressed in closed form for building such a mixed model.…”

Section: Introductionmentioning

confidence: 99%

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

Bodhisuwan

Saengthong

2020

Pak.j.stat.oper.res.

Self Cite

View full text Add to dashboard Cite

In this paper, a new mixed negative binomial (NB) distribution named as negative binomial-weighted Garima (NB-WG) distribution has been introduced for modeling count data. Two special cases of the formulation distribution including negative binomial- Garima (NB-G) and negative binomial-size biased Garima (NB-SBG) are obtained by setting the specified parameter. Some statistical properties such as the factorial moments, the first four moments, variance and skewness have also been derived. Parameter estimation is implemented using maximum likelihood estimation (MLE) and real data sets are discussed to demonstrate the usefulness and applicability of the proposed distribution.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Bodhisuwan

Saengthong

2020

Pak.j.stat.oper.res.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In several studies, it is shown that mixed NB distribution provides better fit on count data compared with the Poisson and the NB distribution. These include the Poisson-inverse Gaussian (Klugman et al, 2008), negative binomial-inverse Gaussian (Gómez-Déniz et al, 2008), negative binomial-Lindley (Zamani & Ismail, 2010), negative binomial-Beta Exponential (Pudprommarat et al, 2012), and negative binomial-Erlang (Kongrod et al, 2014). The Lindley distribution has been generalized by many researchers in recent years.…”

Section: Introductionmentioning

confidence: 99%

The negative binomial-weighted Lindley distribution

Denthet¹,

Promin²

2019

10.5267/j.dsl

View full text Add to dashboard Cite

This paper proposes a new distribution named the negative binomial-weighted Lindley. The study uses the maximum likelihood estimation to estimate the parameters of the proposed distribution and compares the performance of the new method with other distributions. The study finds that the negative binomial-weighted Lindley distribution, obtained by the mixing the negative binomial distribution with the weighted Lindley distribution is another mixed negative binomial distribution and may provide an appropriate fit for data estimation with overdispersion. Some characteristics of the proposed distribution, such as mean and variance are also derived. .

show abstract

“…Several studies have shown that mixed Poisson and mixed NB distributions provide a better fit compared to the Poisson and NB distributions for count data modelling. Some examples of these distributions include the Poissoninverse Gaussian (Klugman et al, 2008), beta-negative binomial (Wang, 2011), negative binomial-beta exponential (Pudprommarat et al, 2012) and negative binomial-generalized exponential distribution (Aryuyuen & Bodhisuwan, 2013). The results of related works show the mixed Poisson and mixed NB distribution can be competitive to the Poisson and NB distributions.…”

Section: Introductionmentioning

confidence: 99%

Bayesian parameters estimation of the negative binomial - two parameter weighted exponential distribution with application in biological data

Prasongporn¹,

Bodhisuwan²

2019

j.appsci

Self Cite

View full text Add to dashboard Cite

The purpose of this paper is to report the results of fitting three biologically significant statistical models, Poisson, negative binomial (NB) and negative binomial-two parameter weighted exponential (NB-TWE) distributions, to biological data. The results of fitting the different distributions are compared by using the K-S Test and Cramer-von Mises Test. We found that the data follows the highest value base on a NB-TWE distribution, which implies that the NB-TWE distribution is the best fit for these data sets. Therefore, the NB-TWE distribution is the best choice for biological data that has an overdispersion problem, and are heavy-tailed with positive skewness.

show abstract

A New Mixed Negative Binomial Distribution

Cited by 21 publications

References 3 publications

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

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

The negative binomial-weighted Lindley distribution

Bayesian parameters estimation of the negative binomial - two parameter weighted exponential distribution with application in biological data

Contact Info

Product

Resources

About