Functional Form for the Generalized Poisson Regression Model

Zamani, Hossein; Ismail, Noriszura

doi:10.1080/03610926.2011.564742

Cited by 29 publications

(22 citation statements)

References 14 publications

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“…Several parameterizations were performed for the generalized Poisson and negative binomial regression models (Famoye et al, 2004;Wang and Famoye, 1997;Zamani and Ismail, 2012;Greene, 2008;Zamani et al, 2014). One of the parameterization of the GP regression model, which is used in this study, was used by Wang and Famoye (1997) for analyzing household fertility count data and by Ismail and Jemain (2007) for analyzing the Malaysian claim count data.…”

Section: Estimator Modelsmentioning

confidence: 99%

Modeling and Forecasting of International Tourism Demand in ASEAN Countries

Karimi¹,

Faroughi²,

Rahim³

2015

American Journal of Applied Sciences

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This study attempts to find the best model to forecast international tourism demand using a series of key macroeconomic variables in ASEAN countries. Generally, we find that generalized Poisson regression model is the best one for estimating long-run international tourism demand. In addition, we find that inflation and real exchange rate have negative relationship with international tourism demand. On the other hand, foreign direct investment and openness of trade have positive relationship with international tourism demand. Cointegration test result shows that there is a long-run relationship between variables.

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Section: Estimator Modelsmentioning

confidence: 99%

Modeling and Forecasting of International Tourism Demand in ASEAN Countries

Karimi¹,

Faroughi²,

Rahim³

2015

American Journal of Applied Sciences

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“…This paper proposes a new BGP-1 regression which is based on GP-1 regression suggested in Zamani and Ismail [12]. GP-1 regression is obtained by replacing …”

Section: Bivariate Generalized Poisson (Bgp) Regressionmentioning

confidence: 99%

“…By excluding zero exposures, we have 547 cross-classified classes of data to be fitted to each set. The same OD claim count data was also analyzed in Zamani and Ismail [12] who fitted univariate GP-P regression. In insurance practices, claim count may give rise to multiple types.…”

Section: Applicationmentioning

confidence: 99%

A new form of bivariate generalized Poisson regression model

Faroughi

Ismail²

2014

AIP Conference Proceedings

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This paper introduces a new form of bivariate generalized Poisson (BGP) regression which can be fitted to bivariate and correlated count data with covariates. The BGP regression suggested in this study can be fitted not only to bivariate count data with positive, zero or negative correlations, but also to underdispersed or overdispersed bivariate count data. Applications of bivariate Poisson (BP) regression and the new BGP regression are illustrated on Malaysian motor insurance data.

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“…The negative binomial regression model has been fitted for overdispersed claim count and count data in other areas by Ismail and Jemain (2007), Lawless (1987), McCullagh and Nelder (1989) and Zulkifli et al (2013). Generalized Poisson regression models have been applied to overdispersed and underdispersed count data by Consul (1989), Consul and Famoye (1992), Wang and Famoye (1997), Zamani and Ismail (2012) and Zamani and Ismail (2014).…”

Section: Introductionmentioning

confidence: 99%