Count-data regression models of the time to adopt new technologies

1998 ◽  
Vol 5 (6) ◽  
pp. 369-373 ◽  
Author(s):  
Bruce Mcwilliams ◽  
Yacov Tsur ◽  
Eithan Hochman ◽  
David Zilberman
Keyword(s):  
Count Data ◽  
Regression Models ◽  
New Technologies ◽  
Data Regression

The Economic Impact of Tourism in Xinghai Park, China: A Travel Cost Value Analysis Using Count Data Regression Models

2009 ◽  
Vol 15 (2) ◽  
pp. 413-425 ◽  
Author(s):  
Erda Wang ◽  
Zuozhi Li ◽  
Bertis B. Little ◽  
Yu Yang
Keyword(s):  
Economic Impact ◽  
Count Data ◽  
Regression Models ◽  
Travel Cost ◽  
Value Analysis ◽  
Data Regression

R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization

1996 ◽  
Vol 14 (2) ◽  
pp. 209 ◽  
Author(s):  
A. Colin Cameron ◽  
Frank A. G. Windmeijer
Keyword(s):  
Health Care ◽  
Health Care Utilization ◽  
Count Data ◽  
Regression Models ◽  
Care Utilization ◽  
Data Regression

Bayesian approach to errors-in-variables in count data regression models with departures from normality and overdispersion

2017 ◽  
Vol 88 (2) ◽  
pp. 203-220 ◽  
Author(s):  
Nur Aainaa Rozliman ◽  
Adriana Irawati Nur Ibrahim ◽  
Rossita Muhamad Yunus
Keyword(s):  
Bayesian Approach ◽  
Count Data ◽  
Regression Models ◽  
Errors In Variables ◽  
Data Regression

scholarly journals Modelling tick bite risk by combining random forests and count data regression models

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0216511 ◽  
Author(s):  
Irene Garcia-Marti ◽  
Raul Zurita-Milla ◽  
Arno Swart
Keyword(s):  
Random Forests ◽  
Count Data ◽  
Regression Models ◽  
Tick Bite ◽  
Data Regression

Generalized hurdle count data regression models

1998 ◽  
Vol 58 (3) ◽  
pp. 263-268 ◽  
Author(s):  
Shiferaw Gurmu
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Data Regression

scholarly journals Application of zero-truncated count data regression models to air-pollution disease

2021 ◽  
Vol 1988 (1) ◽  
pp. 012096
Author(s):  
Z I Zulki Alwani ◽  
A I N Ibrahim ◽  
R M Yunus ◽  
F Yusof
Keyword(s):  
Air Pollution ◽  
Count Data ◽  
Regression Models ◽  
Data Regression

scholarly journals Analysis of Antenatal Care Visit Data in Bangladesh Using Zero Modified Count Regression Model

2019 ◽  
Vol 67 (2) ◽  
pp. 117-122
Author(s):  
Nasiba Maruf Ahmed ◽  
Taslim Sazzad Mallick
Keyword(s):  
Antenatal Care ◽  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Medical Science ◽  
Care Visit ◽  
Data Regression ◽  
Excess Of Zeros ◽  
The Mean ◽  
Education Gap

In medical science, pharmaceutical studies, public health and socio-economic researches we often encounter the situation of excess of zeros in count data. This preponderance of zeros leads to overdispersion. In such cases traditional count data regression models like Poisson and negative binomial (NB) regression may not be pertinent for inference. The two most commonly used types of model that have been developed to adjust for excessivezeros in count data are Hurdle and zero-inflated models. In this study we have analyzed the antenatal care (ANC) visit data of pregnant women in Bangladesh using traditional and zero-modified count models. Based on the model selection criteria, we found that negative binomial hurdle model fits the data best. Through this analysis,we have perceived that the variables age of mother, division, birth order (order a child is born), place of residence, economic condition, media exposure of the mother, mainaccess road to village and education gap between husband and wife have significant impact on the mean number of ANC visits taken. Dhaka Univ. J. Sci. 67(2): 117-122, 2019 (July)

scholarly journals Flexible Bivariate Count Data Regression Models

2012 ◽  
Vol 30 (2) ◽  
pp. 265-274 ◽  
Author(s):  
Shiferaw Gurmu ◽  
John Elder
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Data Regression
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