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Winai Bodhisuwan  One of the best experts on this subject based on the ideXlab platform.

Stochastic Orders Comparisons of Negative Binomial Distribution with Negative Binomial—Lindley Distribution
Open Journal of Statistics, 2020CoAuthors: Chookait Pudprommarat, Winai BodhisuwanAbstract:The purpose of this study is to compare a Negative Binomial distribution with a Negative Binomial—Lindley by using stochastic orders. We characterize the comparisons in usual stochastic order, likelihood ratio order, convex order, expectation order and uniformly more variable order based on theorem and some numerical example of comparisons between Negative Binomial random variable and Negative Binomial—Lindley random variable

The Negative Binomial – Weighted Garima Distribution: Model, Properties and Applications
Pakistan Journal of Statistics and Operation Research, 2020CoAuthors: Winai Bodhisuwan, Pornpop SaengthongAbstract:In this paper, a new mixed Negative Binomial (NB) distribution named as Negative Binomialweighted Garima (NBWG) distribution has been introduced for modeling count data. Two special cases of the formulation distribution including Negative Binomial Garima (NBG) and Negative Binomialsize biased Garima (NBSBG) 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.

Bayesian Inference for the Negative BinomialSushila Linear Model
Lobachevskii Journal of Mathematics, 2019CoAuthors: Darika Yamrubboon, Winai Bodhisuwan, Ampai Thongteeraparp, Katechan Jampachaisri, Andrei VolodinAbstract:The aim of this article is to develop a new linear model for count data. The main idea is in an application of a new generalized linear model framework, which we call the Negative BinomialSushila linear model. The Negative BinomialSushila distribution has been proposed recently and applied to count data. This distribution is constructed as a mixture of the Negative Binomial and Sushila distributions. The mixed distribution is a flexible alternative to the Poisson distribution when overdispersed count data is analyzed. The parameters of this distribution are estimated using a Bayesian approach with R2jags package of the R language. The Negative BinomialSushila linear model is applied to fit two real data sets with an overdispersion and its performance is compared with the performance of some traditional models. The results show that the Negative BinomialSushila generalized linear model fits the data sets better than the traditional generalized models for these data sets.

Zerotruncated Negative Binomial  Erlang distribution
2017CoAuthors: Winai Bodhisuwan, Chookait Pudprommarat, Rujira Bodhisuwan, Luckhana SaothayanunAbstract:The zerotruncated Negative BinomialErlang distribution is introduced. It is developed from Negative BinomialErlang distribution. In this work, the probability mass function is derived and some properties are included. The parameters of the zerotruncated Negative BinomialErlang distribution are estimated by using the maximum likelihood estimation. Finally, the proposed distribution is applied to real data, the number of methamphetamine in the Bangkok, Thailand. Based on the results, it shows that the zerotruncated Negative BinomialErlang distribution provided a better fit than the zerotruncated Poisson, zerotruncated Negative Binomial, zerotruncated generalized NegativeBinomial and zerotruncated PoissonLindley distributions for this data.

Zero inflated Negative BinomialSushila distribution and its application
2017CoAuthors: Darika Yamrubboon, Winai Bodhisuwan, Ampai Thongteeraparp, Katechan JampachaisriAbstract:A new zero inflated distribution is proposed in this work, namely the zero inflated Negative BinomialSushila distribution. The new distribution which is a mixture of the Bernoulli and Negative BinomialSushila distributions is an alternative distribution for the excessive zero counts and overdispersion. Some characteristics of the proposed distribution are derived including probability mass function, mean and variance. The parameter estimation of the zero inflated Negative BinomialSushila distribution is also implemented by maximum likelihood method. In application, the proposed distribution can provide a better fit than traditional distributions: zero inflated Poisson and zero inflated Negative Binomial distributions.
K. Teerapabolarn  One of the best experts on this subject based on the ideXlab platform.

Negative Binomial APPROXIMATION TO THE BETA Binomial DISTRIBUTION
International journal of pure and applied mathematics, 2015CoAuthors: K. TeerapabolarnAbstract:This paper determines a bound on the approximation of the beta Binomial distribution with parameters n, � andby a Negative Binomial distri bution with parametersand �+� �+�+n . With this bound, it is indicated that the beta Binomial distribution can be well approximated by the Negative Binomial distribution whenis large.

Negative Binomial APPROXIMATION TO THE GENERALIZED HYPERGEOMETRIC DISTRIBUTION
International journal of pure and applied mathematics, 2014CoAuthors: K. TeerapabolarnAbstract:This paper uses Stein's method and the wfunction associated with the generalized hypergeometric random variable to determine a bound for the total variation distance between the generalized hypergeometric distribu tion with parameters �, � and N and the Negative Binomial distribution with parameters r = � + 1 and p = 1 − q = �+�+2 �+�+N+1 . In view of this bound, it is observed that the desired result gives a good Negative Binomial approximation whenis large.

POISSON APPROXIMATION FOR INDEPENDENT Negative Binomial RANDOM VARIABLES
International journal of pure and applied mathematics, 2014CoAuthors: K. TeerapabolarnAbstract:We give a bound for the total variation distance between the distribution of a sum of independent Negative Binomial random variables and an appropriate Poisson distribution with mean P n=1 riqi pi , where ri and pi = 1−qi are parameters of each Negative Binomial distribution. It is indicated that the distribution of the sum can be approximated by the Poisson distribution with this mean when each riqi is small.

AN IMPROVED Negative Binomial TO
2014CoAuthors: Olya Distribution, K. TeerapabolarnAbstract:This paper gives an improved Negative Binomial distribution with parameters r and p to approximate the Polya distribution with parameters N, m and r, where p = 1 − q = N N+m . The improved approximation is more accurate than the Negative Binomial approximation when N is sufficiently large.

On the Negative Binomial Approximation to the BetaNegative Binomial Distribution
2008CoAuthors: K. TeerapabolarnAbstract:The wfunction and the Stein identity are adapted and applied to determine an upper bound for the total variation distance between betaNegative Binomial and Negative Binomial distributions. Mathematics Subject Classification: Primary 60F05
Chookait Pudprommarat  One of the best experts on this subject based on the ideXlab platform.

Stochastic Orders Comparisons of Negative Binomial Distribution with Negative Binomial—Lindley Distribution
Open Journal of Statistics, 2020CoAuthors: Chookait Pudprommarat, Winai BodhisuwanAbstract:The purpose of this study is to compare a Negative Binomial distribution with a Negative Binomial—Lindley by using stochastic orders. We characterize the comparisons in usual stochastic order, likelihood ratio order, convex order, expectation order and uniformly more variable order based on theorem and some numerical example of comparisons between Negative Binomial random variable and Negative Binomial—Lindley random variable

Zerotruncated Negative Binomial  Erlang distribution
2017CoAuthors: Winai Bodhisuwan, Chookait Pudprommarat, Rujira Bodhisuwan, Luckhana SaothayanunAbstract:The zerotruncated Negative BinomialErlang distribution is introduced. It is developed from Negative BinomialErlang distribution. In this work, the probability mass function is derived and some properties are included. The parameters of the zerotruncated Negative BinomialErlang distribution are estimated by using the maximum likelihood estimation. Finally, the proposed distribution is applied to real data, the number of methamphetamine in the Bangkok, Thailand. Based on the results, it shows that the zerotruncated Negative BinomialErlang distribution provided a better fit than the zerotruncated Poisson, zerotruncated Negative Binomial, zerotruncated generalized NegativeBinomial and zerotruncated PoissonLindley distributions for this data.

The Negative BinomialSushila Distribution with Application in Count Data Analysis
Thailand Statistician, 2017CoAuthors: Darika Yamrubboon, Chookait Pudprommarat, Winai Bodhisuwan, Luckana SaothayanunAbstract:In this paper, we introduce a Negative BinomialSushila distribution which is a new mixed Negative Binomial distribution. The probability mass function (pmf) has been expressed as mixtures of the Negative Binomial and the Sushila distribution. The factorial moments, the first four moments, variance and skewness have been derived. Moreover, we found that the Negative BinomialLindley distribution is its special case. We also discuss maximum likelihood estimation of the model parameters. For application to real data set, it shows that the new distribution can provide a better fit the data than the Poisson and Negative Binomial distributions. We hope that this distribution may be an alternative model to overdispersed count data analysis.

a new mixed Negative Binomial distribution
Journal of Applied Sciences, 2012CoAuthors: Chookait Pudprommarat, Winai Bodhisuwan, P ZeephongsekulAbstract:A Negative Binomialbeta exponential distribution is a new mixed Negative Binomial distribution obtained by mixing the Negative Binomial distribution with a beta exponential distribution. The generalized Waring and Waring and Yule distributions are presented as special cases of this Negative Binomialbeta exponential distribution. Various structural properties of the new distribution are derived, including expansions for its factorial moments, moments of the order statistics and so forth. We discuss maximum likelihood estimation method for estimating parameters of this distribution. The usefulness of the new distribution is illustrated through a real count data.
Darika Yamrubboon  One of the best experts on this subject based on the ideXlab platform.

Bayesian Inference for the Negative BinomialSushila Linear Model
Lobachevskii Journal of Mathematics, 2019CoAuthors: Darika Yamrubboon, Winai Bodhisuwan, Ampai Thongteeraparp, Katechan Jampachaisri, Andrei VolodinAbstract:The aim of this article is to develop a new linear model for count data. The main idea is in an application of a new generalized linear model framework, which we call the Negative BinomialSushila linear model. The Negative BinomialSushila distribution has been proposed recently and applied to count data. This distribution is constructed as a mixture of the Negative Binomial and Sushila distributions. The mixed distribution is a flexible alternative to the Poisson distribution when overdispersed count data is analyzed. The parameters of this distribution are estimated using a Bayesian approach with R2jags package of the R language. The Negative BinomialSushila linear model is applied to fit two real data sets with an overdispersion and its performance is compared with the performance of some traditional models. The results show that the Negative BinomialSushila generalized linear model fits the data sets better than the traditional generalized models for these data sets.

Zero inflated Negative BinomialSushila distribution and its application
2017CoAuthors: Darika Yamrubboon, Winai Bodhisuwan, Ampai Thongteeraparp, Katechan JampachaisriAbstract:A new zero inflated distribution is proposed in this work, namely the zero inflated Negative BinomialSushila distribution. The new distribution which is a mixture of the Bernoulli and Negative BinomialSushila distributions is an alternative distribution for the excessive zero counts and overdispersion. Some characteristics of the proposed distribution are derived including probability mass function, mean and variance. The parameter estimation of the zero inflated Negative BinomialSushila distribution is also implemented by maximum likelihood method. In application, the proposed distribution can provide a better fit than traditional distributions: zero inflated Poisson and zero inflated Negative Binomial distributions.

The Negative BinomialSushila Distribution with Application in Count Data Analysis
Thailand Statistician, 2017CoAuthors: Darika Yamrubboon, Chookait Pudprommarat, Winai Bodhisuwan, Luckana SaothayanunAbstract:In this paper, we introduce a Negative BinomialSushila distribution which is a new mixed Negative Binomial distribution. The probability mass function (pmf) has been expressed as mixtures of the Negative Binomial and the Sushila distribution. The factorial moments, the first four moments, variance and skewness have been derived. Moreover, we found that the Negative BinomialLindley distribution is its special case. We also discuss maximum likelihood estimation of the model parameters. For application to real data set, it shows that the new distribution can provide a better fit the data than the Poisson and Negative Binomial distributions. We hope that this distribution may be an alternative model to overdispersed count data analysis.
Richard P Waterman  One of the best experts on this subject based on the ideXlab platform.

fixed effects Negative Binomial regression models
Sociological Methodology, 2002CoAuthors: Paul D Allison, Richard P WatermanAbstract:This paper demonstrates that the conditional Negative Binomial model for panel data, proposed by Hausman, Hall, and Griliches (1984), is not a true fixedeffects method. This method—which has been implemented in both Stata and LIMDEP—does not in fact control for all stable covariates. Three alternative methods are explored. A Negative multinomial model yields the same estimator as the conditional Poisson estimator and hence does not provide any additional leverage for dealing with overdispersion. On the other hand, a simulation study yields good results from applying an unconditional Negative Binomial regression estimator with dummy variables to represent the fixed effects. There is no evidence for any incidental parameters bias in the coefficients, and downward bias in the standard error estimates can be easily and effectively corrected using the deviance statistic. Finally, an approximate conditional method is found to perform at about the same level as the unconditional estimator.