Statistical Analysis of Climate Factors Influencing Dengue Incidences in Colombo, Sri Lanka: Poisson and Negative Binomial Regression Approach

2019 ◽  
Vol 9 (2) ◽  
pp. p8616 ◽  
Author(s):  
Leslie Chandrakantha
Keyword(s):  
Statistical Analysis ◽  
Sri Lanka ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Climate Factors ◽  
Factors Influencing ◽  
Binomial Regression ◽  
Regression Approach

scholarly journals An Economic Analysis of Tropical Forest Resource Conservation in a Protected Area

2020 ◽  
Vol 12 (14) ◽  
pp. 5850
Author(s):  
Noel Perceval Assogba ◽  
Daowei Zhang
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Resource Conservation ◽  
Reduced Form ◽  
Negative Binomial Regression Model ◽  
Forest Resource ◽  
Factors Influencing ◽  
Binomial Regression ◽  
Form Model ◽  
Reduced Form Model

In this paper, we develop a reduced form model for factors influencing the conservation of forest resources. We then estimate it using a bivariate negative binomial regression model with cases of illegal farming and illegal cattle grazing in the W Reserve in West Africa. Our results show that population size and farm area in the periphery of the W Reserve are associated with an increase of 2.4% and 7.1% of the illegal farming, respectively. On the other hand, income level, the existence of a checkpoint, and the distance between the villages and the reserve decrease the illegal grazing activities by 7.3%, 63.2%, and 2.3%, respectively.

Heterogeneity and Number of Export Destinations of Italian Firms: A Hurdle Negative Binomial Regression Approach

2013 ◽  
Vol 13 (03n04) ◽  
pp. 391-416 ◽  
Author(s):  
Maria Rosaria Ferrante ◽  
Marco Novelli
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Outcome Variable ◽  
Export Activity ◽  
Multiple Markets ◽  
Binomial Regression ◽  
Regression Approach ◽  
Excess Of Zeros ◽  
Hurdle Regression

This article addresses on an aspect of firms internationalization so far little explored, the choice of the number of export destinations and a proxy of the complexity of the export activity. As the outcome variable is a count with an excess of zeros, we use a hurdle regression model for count data that also allow disentangling the aspect of heterogeneity related to the decision to export from those measuring the number of markets served. Some differences arise by the comparison between the estimates regarding the propensity to export model and those of the model describing the number of export destinations. Regarding the propensity to export, the estimated models support the familiar evidences already presented in literature: exporters are larger, more productive, more innovative and invest more. With reference to the number of export destinations, it seems that not only the larger the number of markets served the more productive, large and willing to invest is the firm but also firms engaged in multiple markets seem to be older, financially stable, and willing to support organizational and managerial innovations.

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