scholarly journals A Bimodal Discrete Shifted Poisson Distribution. A Case Study of Tourists’ Length of Stay

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 442 ◽  
Author(s):  
Emilio Gómez-Déniz ◽  
Jorge Vicente Pérez-Rodríguez ◽  
Jimmy Reyes ◽  
Héctor W. Gómez
Keyword(s):  
Length Of Stay ◽  
Poisson Distribution ◽  
Canary Islands ◽  
Count Data ◽  
Mean Value ◽  
Data Models ◽  
Skewed Data ◽  
Count Data Models ◽  
Empirical Results

Although the Poisson distribution is appropriate for modelling equi-dispersed distributions, it reflects bimodality less well. In this paper, we propose a distribution which is more suitable for the latter purpose. It can be fitted to both positively and negatively skewed data and appears to represent overdispersion phenomena correctly in count data models obtained using a Poisson distribution. Furthermore, the distribution can be normalised in terms of its mean value, and therefore covariates can be included. Our empirical results are based on tourists’ length of stay in the Canary Islands (Spain), a popular holiday destination. The study analyses data supplied by the Canary Islands Tourist Expenditure Survey. Our findings show that the model presented is valid and that the fit obtained is reasonably good.

Modelling Hospital Admission and Length of Stay by Means of Generalised Count Data Models

2015 ◽  
Vol 31 (6) ◽  
pp. 1159-1182 ◽  
Author(s):  
Helmut Herwartz ◽  
Nadja Klein ◽  
Christoph Strumann
Keyword(s):  
Length Of Stay ◽  
Hospital Admission ◽  
Count Data ◽  
Data Models ◽  
Count Data Models

scholarly journals Count data models for demographic data∗

1994 ◽  
Vol 4 (3) ◽  
pp. 205-221 ◽  
Author(s):  
Rainer Winkelmann ◽  
Klaus F. Zimmermann
Keyword(s):  
Count Data ◽  
Demographic Data ◽  
Data Models ◽  
Count Data Models

scholarly journals Analysis of Road Traffic Accidents in the Punjab by Using Panel Count Data Models

2021 ◽  
Vol 3 (1) ◽  
pp. 1-13
Author(s):  
Muhammad Anus Hayat Khan ◽  
Ijaz Hussain
Keyword(s):  
Count Data ◽  
Data Model ◽  
Road Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Data Models ◽  
Panel Count Data ◽  
Road Traffic Accidents ◽  
Count Data Models ◽  
Count Data Model

Each year more than three thousand people die and get serious injuries in traffic accidents. Count data model provide more precise tools for planners and decision makers to conduct proactive road safety planning.We analyzed the exploratory research of Road Traffic Accidents (RTAs) and furthermore explores the factors affecting the RTAs frequency in 36 districts of the Punjab over a time period of three years (July 1, 2013 June 30, 2016) with monthly data using panel count data models. Among the models considered, the random parameters Poisson panel count data model is found to fit the data best. The exploratory analysis shows that highly dense populated districts with large number of registered vehicles causes more accidents as compared to low density populated districts. It is found that, most of the variables used to control the variation in the frequency of RTAs counts play vital role with higher significance levels. The application of regression analysis and modeling of RTAs at district level in Punjab will help to identification of districts with high RTAs rates and this could help more efficient road safety management in the Punjab.

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