Negative Binomial

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform

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

K. Teerapabolarn - One of the best experts on this subject based on the ideXlab platform.

Chookait Pudprommarat - One of the best experts on this subject based on the ideXlab platform.

Darika Yamrubboon - One of the best experts on this subject based on the ideXlab platform.

  • Bayesian Inference for the Negative Binomial-Sushila Linear Model
    Lobachevskii Journal of Mathematics, 2019
    Co-Authors: Darika Yamrubboon, Winai Bodhisuwan, Ampai Thongteeraparp, Katechan Jampachaisri, Andrei Volodin
    Abstract:

    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 Binomial-Sushila linear model. The Negative Binomial-Sushila 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 over-dispersed count data is analyzed. The parameters of this distribution are estimated using a Bayesian approach with R2jags package of the R language. The Negative Binomial-Sushila linear model is applied to fit two real data sets with an over-dispersion and its performance is compared with the performance of some traditional models. The results show that the Negative Binomial-Sushila generalized linear model fits the data sets better than the traditional generalized models for these data sets.

  • Zero inflated Negative Binomial-Sushila distribution and its application
    2017
    Co-Authors: Darika Yamrubboon, Winai Bodhisuwan, Ampai Thongteeraparp, Katechan Jampachaisri
    Abstract:

    A new zero inflated distribution is proposed in this work, namely the zero inflated Negative Binomial-Sushila distribution. The new distribution which is a mixture of the Bernoulli and Negative Binomial-Sushila distributions is an alternative distribution for the excessive zero counts and over-dispersion. Some characteristics of the proposed distribution are derived including probability mass function, mean and variance. The parameter estimation of the zero inflated Negative Binomial-Sushila 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 Binomial-Sushila Distribution with Application in Count Data Analysis
    Thailand Statistician, 2017
    Co-Authors: Darika Yamrubboon, Chookait Pudprommarat, Winai Bodhisuwan, Luckana Saothayanun
    Abstract:

    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.

Richard P Waterman - One of the best experts on this subject based on the ideXlab platform.

  • fixed effects Negative Binomial regression models
    Sociological Methodology, 2002
    Co-Authors: Paul D Allison, Richard P Waterman
    Abstract:

    This paper demonstrates that the conditional Negative Binomial model for panel data, proposed by Hausman, Hall, and Griliches (1984), is not a true fixed-effects 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 over-dispersion. 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.