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* = applied session ! = JSM meeting theme
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Keyword Search Criteria: count data returned 24 record(s)
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Monday, 07/31/2017
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Constrained Longitudinal Model for Analyzing Pre- and Post-Treatment Count Data
Yongming Qu, Eli Lilly and Company; Junxiang Luo, Eli Lilly and Company; Hong Zhao, Abbott Laboratories
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A Bayesian Mixture Model for Clustering and Selection of Feature Occurrence Rates Under Mean Constraints
Qiwei Li, Rice University; Michele Guindani, University of California, Irvine; Brian Reich, NCSU; Howard Bondell, NC State University; Marina Vannucci, Rice University
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Objective Bayesian Modeling of Count Data
Si Cheng, University of Cincinnati; Xia Wang, University of Cincinnati
8:50 AM
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A Bayesian Mixture Model for Clustering and Selection of Feature Occurrence Rates Under Mean Constraints
Qiwei Li, Rice University; Michele Guindani, University of California, Irvine; Brian Reich, NCSU; Howard Bondell, NC State University; Marina Vannucci, Rice University
9:15 AM
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Composition Estimation from Sparse Count Data via a Regularized Likelihood
Hongzhe Li, University of Pennsylvania; Yuanpei Cao, University of Pennsylvania; Anru Zhang, University of Wisconsin-Madison
10:35 AM
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Modeling Count Data from a Complex Survey using Pseudo Maximum Likelihood
Mulugeta Gebregziabher, MUSC; Lin Dai, Medical University of South Carolina
10:55 AM
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West African Ebola, Zika in the Americas and Direct Likelihood-Based Inference for Stochastic Compartmental Models
Marc A. Suchard, University of California, Los Angeles
11:00 AM
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Bayesian Analysis of Hierarchical Mixed Effect Hurdle Model for Time and Spatially Correlated Count Data
Soutik Ghosal, University of Louisville; Maiying Kong, University of Louisville; Jeremy Gaskins, University of Louisville
2:05 PM
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Tuesday, 08/01/2017
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A Conditionally Autoregressive-Spatial Model for Clustered Count Data Controlling for Two Classes of Neighbor Relations
Jason Clague, University of California Los Angeles; Thomas Belin, University of California, Los Angeles; Vivek Shetty, University of California Los Angeles
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Sample Size Calculation for the Generalized Poisson Regression Model Comparing Two Rates of Recurrent Events
Kimitoshi Ikeda, Amgen Astellas BioPharma K.K.
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Small Area Models for Over-Dispersed Poisson Counts
Jerry Maples, U.S. Census Bureau; Adam Maidman, University of Minnesota
8:55 AM
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Sample Size Calculation for the Generalized Poisson Regression Model Comparing Two Rates of Recurrent Events
Kimitoshi Ikeda, Amgen Astellas BioPharma K.K.
9:50 AM
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Developments and Comparisons Between Markov Chain Monte Carlo and Integrated Nested Laplace Approximations in the Censored Poisson Model
Xiao Yu, University of Texas Health Science Center at Houston; Lung-chang Chien, University of Nevada, Las Vegas
10:05 AM
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Adjusting for Overdispersion in Microbiome Data Analysis
Ekaterina Smirnova, University of Montana; Glen Satten, Centers for Disease Control and Prevention
10:35 AM
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Testing Hypotheses About the Microbiome Using a New Ordination-Based Linear Model
Glen Satten, Centers for Disease Control and Prevention; Yijuan Hu, Emory University
11:55 AM
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T-Geometric Regression Models with Applications to Zero-Inflated Count Data
Carl Lee, Central Michigan Univ; Felix Famoye, Central Michigan University; Alfred Akinsete, Marshall University
2:25 PM
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Generalized Mixed Effects Regression Tree for Longitudinal Count Data
Xiaoqin Xiong, Food and Drug Administration; Joel A Dubin, University of Waterloo
3:20 PM
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Wednesday, 08/02/2017
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Modeling Overdispersed Count Data with the Poisson-Inverse Gaussian Distribution
Kimberly Weems, North Carolina Central University; Paul Smith, University of Maryland
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Zero-Inflated Model: Is it Sufficient for Estimating Excess Zeros?
Charles Rose, CDC
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Zero-Inflated Model: Is it Sufficient for Estimating Excess Zeros?
Charles Rose, CDC
8:35 AM
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Inflated Values Selection Method on Multiple-Inflated Poisson Model
Qiuya Li; Kwok Fai Geoffrey TSO, City University of Hong Kong; Yichen Qin, University of Cincinnati; Yang Li, Renmin University of China
10:35 AM
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A Score Test for Over-Dispersion in Marginalized Zero-Inflated Poisson Regression Models
Gul Inan, Middle East Technical University; John Preisser, University of North Carolina-Chapel Hill; Kalyan Das, University of Calcutta
11:35 AM
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