A Novel Hierarchical Statistical Model for Count Data Modeling and Its Application in Image Classification

2012 ◽  
pp. 332-340 ◽  
Author(s):  
Ali Shojaee Bakhtiari ◽  
Nizar Bouguila
Keyword(s):  
Statistical Model ◽  
Image Classification ◽  
Count Data ◽  
Data Modeling ◽  
Hierarchical Statistical Model

Frequency of Change Orders in Highway Construction Using Alternate Count-Data Modeling Methods

2010 ◽  
Vol 136 (8) ◽  
pp. 886-893 ◽  
Author(s):  
Panagiotis Ch. Anastasopoulos ◽  
Samuel Labi ◽  
Abhishek Bhargava ◽  
Claire Bordat ◽  
Fred L. Mannering
Keyword(s):  
Count Data ◽  
Highway Construction ◽  
Data Modeling ◽  
Change Orders ◽  
Modeling Methods

A New Utility-Consistent Econometric Approach to Multivariate Count Data Modeling

2014 ◽  
Vol 30 (5) ◽  
pp. 806-825 ◽  
Author(s):  
Chandra R. Bhat ◽  
Rajesh Paleti ◽  
Marisol Castro
Keyword(s):  
Count Data ◽  
Data Modeling ◽  
Econometric Approach

scholarly journals A hierarchical statistical model for estimating population properties of quantitative genes

BMC Genetics ◽  
2002 ◽  
Vol 3 (1) ◽  
Author(s):  
Samuel S Wu ◽  
Chang-Xing Ma ◽  
Rongling Wu ◽  
George Casella
Keyword(s):  
Statistical Model ◽  
Hierarchical Statistical Model

scholarly journals Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts

2017 ◽  
Vol 51 (3) ◽  
pp. 198-208 ◽  
Author(s):  
John S. Preisser ◽  
D. Leann Long ◽  
John W. Stamm
Keyword(s):  
Clinical Trial ◽  
Dental Caries ◽  
Statistical Model ◽  
Randomized Clinical Trial ◽  
Count Data ◽  
Treatment Effects ◽  
Negative Binomial ◽  
Research Question ◽  
Model Class ◽  
Zero Counts

Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two data sets, one consisting of fictional dmft counts in 2 groups and the other on DMFS among schoolchildren from a randomized clinical trial comparing 3 toothpaste formulations to prevent incident dental caries, are analyzed with negative binomial hurdle, zero-inflated negative binomial, and marginalized zero-inflated negative binomial models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the randomized clinical trial were similar despite their distinctive interpretations. The choice of statistical model class should match the study's purpose, while accounting for the broad decline in children's caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts.

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