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Relevant bibliographies by topics / Negative binomial regression model

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Journal articles

Academic literature on the topic 'Negative binomial regression model'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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Journal articles on the topic "Negative binomial regression model"

1

Cepeda-Cuervo, Edilberto, and María Victoria Cifuentes-Amado. "Double Generalized Beta-Binomial and Negative Binomial Regression Models." Revista Colombiana de Estadística 40, no. 1 (2017): 141–63. http://dx.doi.org/10.15446/rce.v40n1.61779.

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Overdispersion is a common phenomenon in count datasets, that can greatly affect inferences about the model. In this paper develop three joint mean and dispersion regression models in order to fit overdispersed data. These models are based on reparameterizations of the beta-binomial and negative binomial distributions. Finally, we propose a Bayesian approach to estimate the parameters of the overdispersion regression models and use it to fit a school absenteeism dataset.

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2

Famoye, Felix. "On the bivariate negative binomial regression model." Journal of Applied Statistics 37, no. 6 (2010): 969–81. http://dx.doi.org/10.1080/02664760902984618.

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3

Xue, Dixi, and James A. Deddens. "Overdispersed negative binomial regression models." Communications in Statistics - Theory and Methods 21, no. 8 (1992): 2215–26. http://dx.doi.org/10.1080/03610929208830908.

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4

Li, Chin-Shang. "Semiparametric Negative Binomial Regression Models." Communications in Statistics - Simulation and Computation 39, no. 3 (2010): 475–86. http://dx.doi.org/10.1080/03610910903480834.

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5

Hung, Lai-Fa. "A Negative Binomial Regression Model for Accuracy Tests." Applied Psychological Measurement 36, no. 2 (2012): 88–103. http://dx.doi.org/10.1177/0146621611429548.

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Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an overdispersion framework and proposes new estimation methods. The parameters in the proposed model can be estimated using the Markov chain Monte Carlo method implemented in WinBUGS and the marginal maximum likelihood method implemented in SAS. An empirical example based on models generated by the results of empirical data, which are fitted and discussed, is examined.

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6

D’Andrea, Amanda, Ricardo Rocha, Vera Tomazella, and Francisco Louzada. "Negative Binomial Kumaraswamy-G Cure Rate Regression Model." Journal of Risk and Financial Management 11, no. 1 (2018): 6. http://dx.doi.org/10.3390/jrfm11010006.

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7

Rashad, Nadwa Khazaal, Nawal Mahmood Hammood, and Zakariya Yahya Algamal. "Generalized ridge estimator in negative binomial regression model." Journal of Physics: Conference Series 1897, no. 1 (2021): 012019. http://dx.doi.org/10.1088/1742-6596/1897/1/012019.

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8

Allison, Paul D., and Richard P. Waterman. "7. Fixed-Effects Negative Binomial Regression Models." Sociological Methodology 32, no. 1 (2002): 247–65. http://dx.doi.org/10.1111/1467-9531.00117.

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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.

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9

Faroughi, Pouya, and Noriszura Ismail. "Bivariate zero-inflated negative binomial regression model with applications." Journal of Statistical Computation and Simulation 87, no. 3 (2016): 457–77. http://dx.doi.org/10.1080/00949655.2016.1213843.

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10

Türkan, Semra, and Gamze Özel. "A Jackknifed estimators for the negative binomial regression model." Communications in Statistics - Simulation and Computation 47, no. 6 (2017): 1845–65. http://dx.doi.org/10.1080/03610918.2017.1327069.

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