The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Winai Bodhisuwan - One of the best experts on this subject based on the ideXlab platform.
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Zero-truncated negative Binomial - Erlang distribution
2017Co-Authors: Winai Bodhisuwan, Chookait Pudprommarat, Rujira Bodhisuwan, Luckhana SaothayanunAbstract:The zero-truncated negative Binomial-Erlang distribution is introduced. It is developed from negative Binomial-Erlang distribution. In this work, the probability mass function is derived and some properties are included. The parameters of the zero-truncated negative Binomial-Erlang 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 zero-truncated negative Binomial-Erlang distribution provided a better fit than the zero-truncated Poisson, zero-truncated negative Binomial, zero-truncated generalized negative-Binomial and zero-truncated Poisson-Lindley distributions for this data.
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Zero-truncated negative Binomial - Erlang distribution
2017Co-Authors: Winai Bodhisuwan, Chookait Pudprommarat, Rujira Bodhisuwan, Luckhana SaothayanunAbstract:The zero-truncated negative Binomial-Erlang distribution is introduced. It is developed from negative Binomial-Erlang distribution. In this work, the probability mass function is derived and some properties are included. The parameters of the zero-truncated negative Binomial-Erlang 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 zero-truncated negative Binomial-Erlang distribution provided a better fit than the zero-truncated Poisson, zero-truncated negative Binomial, zero-truncated generalized negative-Binomial and zero-truncated Poisson-Lindley distributions for this data.
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The Negative Binomial-Sushila Distribution with Application in Count Data Analysis
Thailand Statistician, 2017Co-Authors: Darika Yamrubboon, Chookait Pudprommarat, Winai Bodhisuwan, Luckhana SaothayanunAbstract:In this paper, we introduce a negative Binomial-Sushila 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 Binomial-Lindley 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 over-dispersed count data analysis.
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THE NEGATIVE Binomial-ERLANG DISTRIBUTION WITH APPLICATIONS
International journal of pure and applied mathematics, 2014Co-Authors: Siriporn Kongrod, Winai Bodhisuwan, Prasit PayakkapongAbstract:This paper introduces a new three-parameter of the mixed negative Binomial distribution which is called the negative Binomial-Erlang distribution. This distribution obtained by mixing the negative Binomial distribution with the Erlang distribution. The negative Binomial-Erlang distribution can be used to describe count data with a large number of zeros. The negative Binomial- exponential is presented as special cases of the negative Binomial-Erlang distri- bution. In addition, we present some properties of the negative Binomial-Erlang distribution including factorial moments, mean, variance, skewness and kurto- sis. The parameter estimation for negative Binomial-Erlang distribution by the maximum likelihood estimation are provided. Applications of the the nega- tive Binomial-Erlang distribution are carried out two real count data sets. The result shown that the negative Binomial-Erlang distribution is better than fit when compared the Poisson and negative Binomial distributions.
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Parameters Estimation Methods for the Negative Binomial-Crack Distribution and Its Application
2014Co-Authors: Pornpop Saengthong, Winai BodhisuwanAbstract:Abstract In this paper we study four parameters negative Binomial-Crack (NB-CR) distribution. This new formulation distribution contains as special cases three parameters distribution, namely, negative Binomial-inverse Gaussian (NB-IG), negative Binomial-Birnbaum-Saunders (NB-BS) and negative Binomial-length biased inverse Gaussian (NB-LBIG). The objective of our research is to estimate the parameters for NB-CR distribution by using maximum likelihood estimation and the method of moments. These methods are illustrated with an application to accident data. Keywords: negative Binomial-Crack distribution, parameter estimation, maximum likelihood estimation, method of moments count data
Chookait Pudprommarat - One of the best experts on this subject based on the ideXlab platform.
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Zero-truncated negative Binomial - Erlang distribution
2017Co-Authors: Winai Bodhisuwan, Chookait Pudprommarat, Rujira Bodhisuwan, Luckhana SaothayanunAbstract:The zero-truncated negative Binomial-Erlang distribution is introduced. It is developed from negative Binomial-Erlang distribution. In this work, the probability mass function is derived and some properties are included. The parameters of the zero-truncated negative Binomial-Erlang 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 zero-truncated negative Binomial-Erlang distribution provided a better fit than the zero-truncated Poisson, zero-truncated negative Binomial, zero-truncated generalized negative-Binomial and zero-truncated Poisson-Lindley distributions for this data.
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Zero-truncated negative Binomial - Erlang distribution
2017Co-Authors: Winai Bodhisuwan, Chookait Pudprommarat, Rujira Bodhisuwan, Luckhana SaothayanunAbstract:The zero-truncated negative Binomial-Erlang distribution is introduced. It is developed from negative Binomial-Erlang distribution. In this work, the probability mass function is derived and some properties are included. The parameters of the zero-truncated negative Binomial-Erlang 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 zero-truncated negative Binomial-Erlang distribution provided a better fit than the zero-truncated Poisson, zero-truncated negative Binomial, zero-truncated generalized negative-Binomial and zero-truncated Poisson-Lindley distributions for this data.
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The Negative Binomial-Sushila Distribution with Application in Count Data Analysis
Thailand Statistician, 2017Co-Authors: Darika Yamrubboon, Chookait Pudprommarat, Winai Bodhisuwan, Luckhana SaothayanunAbstract:In this paper, we introduce a negative Binomial-Sushila 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 Binomial-Lindley 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 over-dispersed count data analysis.
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a new mixed negative Binomial distribution
Journal of Applied Sciences, 2012Co-Authors: Chookait Pudprommarat, Winai Bodhisuwan, Panlop ZeephongsekulAbstract:A negative Binomial-beta 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 Binomial-beta 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.
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Stochastic Orders Comparisons of Negative Binomial Distribution with Negative Binomial—Lindley Distribution
Open Journal of Statistics, 2012Co-Authors: 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
Luckhana Saothayanun - One of the best experts on this subject based on the ideXlab platform.
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Zero-truncated negative Binomial - Erlang distribution
2017Co-Authors: Winai Bodhisuwan, Chookait Pudprommarat, Rujira Bodhisuwan, Luckhana SaothayanunAbstract:The zero-truncated negative Binomial-Erlang distribution is introduced. It is developed from negative Binomial-Erlang distribution. In this work, the probability mass function is derived and some properties are included. The parameters of the zero-truncated negative Binomial-Erlang 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 zero-truncated negative Binomial-Erlang distribution provided a better fit than the zero-truncated Poisson, zero-truncated negative Binomial, zero-truncated generalized negative-Binomial and zero-truncated Poisson-Lindley distributions for this data.
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Zero-truncated negative Binomial - Erlang distribution
2017Co-Authors: Winai Bodhisuwan, Chookait Pudprommarat, Rujira Bodhisuwan, Luckhana SaothayanunAbstract:The zero-truncated negative Binomial-Erlang distribution is introduced. It is developed from negative Binomial-Erlang distribution. In this work, the probability mass function is derived and some properties are included. The parameters of the zero-truncated negative Binomial-Erlang 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 zero-truncated negative Binomial-Erlang distribution provided a better fit than the zero-truncated Poisson, zero-truncated negative Binomial, zero-truncated generalized negative-Binomial and zero-truncated Poisson-Lindley distributions for this data.
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The Negative Binomial-Sushila Distribution with Application in Count Data Analysis
Thailand Statistician, 2017Co-Authors: Darika Yamrubboon, Chookait Pudprommarat, Winai Bodhisuwan, Luckhana SaothayanunAbstract:In this paper, we introduce a negative Binomial-Sushila 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 Binomial-Lindley 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 over-dispersed count data analysis.
Jan Van Den Broek - One of the best experts on this subject based on the ideXlab platform.
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Overdispersion in clinical mastitis ata from dairy herds: a negative Binomial approach
Preventive Veterinary Medicine, 1991Co-Authors: Ynte H. Schukken, Giovanni Casella, Jan Van Den BroekAbstract:Abstract Cases of clinical mastitis on diary farms were modelled using a negative Binomial distribution. The negative Binomial distribution is a gamma mixture of Poisson distributions, hence it is possible that the number of cases per farm had a Poisson distributions and the mean of these Poisson distributions had a gamma distribution. Risk factors associated with the number of cases per farm can be modelled with a negative Binomial distribution but calculations involving the negative Binomial likelihood are not straightforward. However, a very close approximation to the negative Binomial is the Poisson model with an extra parameter for overdispersion. Fitting the Poisson model with overdispersion is illustrated.
Joseph M. Hilbe - One of the best experts on this subject based on the ideXlab platform.
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Negative Binomial regression: modeling
Negative Binomial Regression, 2011Co-Authors: Joseph M. HilbeAbstract:In this chapter we describe how count response data can be modeled using the NB2 negative Binomial regression. NB2 is the traditional parameterization of the negative Binomial model, and is the one with which most statisticians are familiar. For this chapter, then, any reference to negative Binomial regression will be to the NB2 model unless otherwise indicated. Poisson versus negative Binomial We have earlier stated that, given the direct relationship in the negative Binomial variance between α and the fitted value, μ, the model becomes Poisson as the value of α approaches zero. A negative Binomial with α = 0 will not converge because of division by zero, but values close to zero allow convergence. Where α is close to zero, the model statistics displayed in Poisson output are nearly the same as those of a negative Binomial. The relationship can be observed using simulated data. The code below constructs a 50,000 observation synthetic Poisson model with an intercept value of 2 and parameter values of x 1 = 0.75 and x 2 = 1.25. Each predictor is generated as a synthetic random normal variate. The Poisson data are then modeled using a negative Binomial where the value of α is estimated prior to the calculation of parameter estimates and associated statistics.
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Negative Binomial Regression: Negative Binomial regression: modeling
2007Co-Authors: Joseph M. HilbeAbstract:In this chapter we describe how count response data can be modeled using the NB2 negative Binomial regression. NB2 is the traditional parameterization of the negative Binomial model, and is the one with which most statisticians are familiar. For this chapter, then, any reference to negative Binomial regression will be to the NB2 model unless otherwise indicated. Poisson versus negative Binomial We have earlier stated that, given the direct relationship in the negative Binomial variance between α and the fitted value, μ, the model becomes Poisson as the value of α approaches zero. A negative Binomial with α = 0 will not converge because of division by zero, but values close to zero allow convergence. Where α is close to zero, the model statistics displayed in Poisson output are nearly the same as those of a negative Binomial. The relationship can be observed using simulated data. The code below constructs a 50,000 observation synthetic Poisson model with an intercept value of 2 and parameter values of x 1 = 0.75 and x 2 = 1.25. Each predictor is generated as a synthetic random normal variate. The Poisson data are then modeled using a negative Binomial where the value of α is estimated prior to the calculation of parameter estimates and associated statistics.