scholarly journals Marginalized zero-inflated Poisson models with missing covariates

2018 ◽  
Vol 60 (4) ◽  
pp. 845-858
Author(s):  
Habtamu K. Benecha ◽  
John S. Preisser ◽  
Kimon Divaris ◽  
Amy H. Herring ◽  
Kalyan Das
Keyword(s):  
Missing Covariates ◽  
Poisson Models ◽  
Zero Inflated Poisson Models

scholarly journals A study of the score test in discrimination poisson and zero-inflated poisson models

2013 ◽  
Vol 35 (2) ◽  
Author(s):  
Vanessa Siqueira Peres da Silva ◽  
Marcelo Angelo Cirillo ◽  
Juliana Garcia Cespedes
Keyword(s):  
Score Test ◽  
Poisson Models ◽  
Zero Inflated Poisson Models

scholarly journals Investigating Relationships Between Drinking Venues, Drinking Companions, and Corporal Punishment of Children

2018 ◽  
Vol 24 (1) ◽  
pp. 45-55
Author(s):  
Jennifer Price Wolf ◽  
Elinam D. Dellor
Keyword(s):  
Alcohol Use ◽  
Corporal Punishment ◽  
Mixed Mode ◽  
Alcohol Screening ◽  
Screening Tools ◽  
Physical Discipline ◽  
Limited Evidence ◽  
Poisson Models ◽  
Drinking Frequency ◽  
Zero Inflated Poisson Models

Limited evidence suggests that how much a parent drinks in a particular venue, such as a bar, restaurant, or a friend’s home, is associated with use of corporal punishment. However, these relationships could differ depending on their drinking companions (e.g., spouse or friends). In this study, weighted zero-inflated Poisson models were used to examine whether the relationships between venue-specific drinking frequency, heavier drinking, and corporal punishment are moderated by drinking companions in a mixed-mode sample of parents ( n = 1,599). The relationships between drinking frequency, heavier drinking, and corporal punishment varied by drinking companions, with some combinations being protective and others conferring risk. While most alcohol screening tools focus on individual alcohol use, more nuanced assessment examining where and with whom parents are drinking could be helpful in understanding risk of physical discipline.

scholarly journals Tree Height Increment Models for National Forest Inventory Data in the Pacific Northwest, USA

Forests ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 2
Author(s):  
Hyeyoung Woo ◽  
Bianca N. I. Eskelson ◽  
Vicente J. Monleon
Keyword(s):  
Pacific Northwest ◽  
Tree Height ◽  
The United States ◽  
Tree Level ◽  
Height Increment ◽  
Inventory Data ◽  
National Inventory ◽  
Poisson Models ◽  
The Pacific ◽  
Zero Inflated Poisson Models

The United States national inventory program measures a subset of tree heights in each plot in the Pacific Northwest. Unmeasured tree heights are predicted by adding the difference between modeled tree heights at two measurements to the height observed at the first measurement. This study compared different approaches for directly modeling 10-year height increment of red alder (RA) and ponderosa pine (PP) in Washington and Oregon using national inventory data from 2001–2015. In addition to the current approach, five models were implemented: nonlinear exponential, log-transformed linear, gamma, quasi-Poisson, and zero-inflated Poisson models using both tree-level (e.g., height, diameter at breast height, and compacted crown ratio) and plot-level (e.g., basal area, elevation, and slope) measurements as predictor variables. To account for negative height increment observations in the modeling process, a constant was added to shift all response values to greater than zero (log-transformed linear and gamma models), the negative increment was set to zero (quasi-Poisson and zero-inflated Poisson models), or a nonlinear model, which allows negative observations, was used. Random plot effects were included to account for the hierarchical data structure of the inventory data. Predictive model performance was examined through cross-validation. Among the implemented models, the gamma model performed best for both species, showing the smallest root mean square error (RSME) of 2.61 and 1.33 m for RA and PP, respectively (current method: RA—3.33 m, PP—1.40 m). Among the models that did not add the constant to the response, the quasi-Poisson model exhibited the smallest RMSE of 2.74 and 1.38 m for RA and PP, respectively. Our study showed that the prediction of tree height increment in Oregon and Washington can be improved by accounting for the negative and zero height increment values that are present in inventory data, and by including random plot effects in the models.

scholarly journals A test of inflated zeros for Poisson regression models

2017 ◽  
Vol 28 (4) ◽  
pp. 1157-1169 ◽  
Author(s):  
Hua He ◽  
Hui Zhang ◽  
Peng Ye ◽  
Wan Tang
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
Type I Error ◽  
Large Body ◽  
Poisson Model ◽  
Type I ◽  
New Approach ◽  
Poisson Models ◽  
Zero Inflated Poisson Models ◽  
Vuong Test

Excessive zeros are common in practice and may cause overdispersion and invalidate inference when fitting Poisson regression models. There is a large body of literature on zero-inflated Poisson models. However, methods for testing whether there are excessive zeros are less well developed. The Vuong test comparing a Poisson and a zero-inflated Poisson model is commonly applied in practice. However, the type I error of the test often deviates seriously from the nominal level, rendering serious doubts on the validity of the test in such applications. In this paper, we develop a new approach for testing inflated zeros under the Poisson model. Unlike the Vuong test for inflated zeros, our method does not require a zero-inflated Poisson model to perform the test. Simulation studies show that when compared with the Vuong test our approach not only better at controlling type I error rate, but also yield more power.

Simulating comparisons of different computing algorithms fitting zero-inflated Poisson models for zero abundant counts

2017 ◽  
Vol 87 (13) ◽  
pp. 2609-2621 ◽  
Author(s):  
Xueyan Liu ◽  
Bryan Winter ◽  
Li Tang ◽  
Bo Zhang ◽  
Zhiwei Zhang ◽  
...  
Keyword(s):  
Poisson Models ◽  
Zero Inflated Poisson Models

Number of Accidents or Number of Claims? An Approach with Zero-Inflated Poisson Models for Panel Data

2009 ◽  
Vol 76 (4) ◽  
pp. 821-846 ◽  
Author(s):  
Jean-Philippe Boucher ◽  
Michel Denuit ◽  
Montserrat Guillen
Keyword(s):  
Panel Data ◽  
Poisson Models ◽  
Zero Inflated Poisson Models
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