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CC = Vancouver Convention Centre   F = Fairmont Waterfront Vancouver
* = applied session       ! = JSM meeting theme

Activity Details

254 Mon, 7/30/2018, 2:00 PM - 3:50 PM CC-West Hall B
Contributed Poster Presentations: Section on Statistical Learning and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Paul McNicholas, McMaster University
31: Computing Mean Partition and Assessing Uncertainty for Clustering Analysis
Beomseok Seo, Penn State University; Lin Lin, The Pennsylvania State University; Jia Li, Penn State University
32: A Generalized Fellegi-Sunter Framework for Unsupervised Collective Record Linkage in Clustered Relational Data with Applications to Electronic Health Records
Nicole Solomon, Duke University Medical Center; Sean M O'Brien, Duke University Medical Center; Joseph Lucas, Duke University
33: Predictive Big Data Analytics in Mental Disorders Using the UK Biobank
Yiwang Zhou, University of Michigan; Ivo Dinov, Statistics Online Computational Resource, University of Michigan; Simeone Marino, Statistics Online Computational Resource, University of Michigan
34: Sparse Variable Selection in Kernel Discriminant Analysis via Optimal Scoring
Alexander Lapanowski, Texas A&M; Irina Gaynanova, Texas A&M Univeristy
35: An Application of Clustering Method on EHR Data Phenotyping and Prediction
Shu Wang, University of Pittsburgh; Joyce Chung-Chou H Chang, University of Pittsburgh; Christopher W. Seymour, University of Pittsburgh; Jason Kennedy, University of Pittsburgh; Zhongying Xu, University of Pittsburgh
36: An Algorithm to Compare Patterns and Its Application on Shoe Out-Sole Impressions
Soyoung Park, Iowa State University / CSAFE; Alicia Carriquiry, Iowa State University
37: Predicting Hospital Readmission for Diabetes Patients by Classical and Machine Learning Approaches
Gabrielle LaRosa, University of Pittsburgh; Chathurangi Pathiravsan, Southern Illinois University Carbondale; Rajapaksha Wasala M Anusha Madushani, University of Florida
38: The Classification of Stellar Systems Through Singular Spectrum Analysis
Kevin Matheson, Western Washington University; Kevin Covey, Western Washington University; Kimihiro Noguchi, Western Washington University
39: Machine Learning with Ensemble Feature Selections for Mass Spectrometry Data in Cancer Study
Yulan Liang, University of Maryland Baltimore; Amin Gharipour, Griffith University; Arpad Kelemen, University of Maryland Baltimore; Adam Kelemen, University of Maryland College Park; Hui Zhang, Johns Hopkins Medical Institutions
40: Structured Mixture of Linear Mappings in High Dimension
Chun-Chen Tu, University of Michigan; Florence Forbes, INRIA; Benjamin Lemasson, Universit ´e Grenoble; Naisyin Wang, U of Michigan
44: A Generalization of Convolutional Neural Networks to Graph-Structured Data
Yotam Hechtlinger, Carnegie Mellon Univ; Purvasha Chakravarti, Carnegie Mellon University; Jining Qin, Carnegie Mellon University
45: Empirical Evaluation for Platt Scaling and Isotonic Regression
Weihua Shi, SAS Institute, Inc.
47: Graphical Model for Continuous Longitudinal Data
Lei Wang, The University of Queensland
48: The Application of Elastic Net with Fused Term in Change Point Detection via Coordinate Descent
Zhi Wang, University of Alabama
49: Global Sensitivity Analysis from Given Data : Elementary Effect Approach
Jong hyun Kim, Hanyang University; Dae il Jang, Hanyang University; Kyung joon Cha, Hanyang University
50: Per-Gene Normalization Method (UQ-PgQ2) Improves the Specificity for the Analysis of Differential Gene Expression in RNA-Seq Data
Xiaohong Li, University of Louisville; Nigel G.F. Cooper, University of Louisville; Dongfeng Wu, University of Louisville; Eric C. Rouchka, University of Louisville; Shesh N. Rai, University of Louisville
51: Multivariate Zero-Inflated Poisson Regression
Yang Wang, University of Alabama
52: Sound and Solid Selection of Covariates - a Simulation Study
Kira Dynnes Svendsen, Technical University of Denmark; Nina Munkholt Jakobsen, Technical University of Denmark
53: Machine-Learning Approach to Defining Covariates to Increase Study Power in ALS Clinical Trials and Other Multifactorial Heterogeneous Disease Areas
Danielle Beaulieu, Origent Data Sciences; Albert Taylor, Origent Data Sciences; Samad Jahandideh, Origent Data Sciences; David Ennist, Origent Data Sciences; Andrew Conklin, Origent Data Sciences; Mike Keymer, Origent Data Sciences
54: Functional Graphical Model Classification
Peide Li
55: Variation of Functional Connectome Topology and Its Implications for Attention
Kelson Zawack, Yale University
56: Model-Based Clustering of Time-Dependent Categorical Sequence
Yingying Zhang, The University of Alabama; Volodymyr Melnykov, University of Alabama
57: Learning an Interpretable Behavioral Intervention Policy Using MHealth Data
Xinyu Hu, Columbia University; Min Qian, Columbia University; Ying Kuen Ken Cheung, Columbia University
58: Spatial and Temporal Trends in Weather Forecasting and Improving Predictions with ARIMA Modeling
Manasi Sheth, California State University; Mahalaxmi Gundreddy, California State University East Bay; Vivek Shah, Applied Materials, Inc. ; Pritam Barlota, California State University East Bay; Eric Suess, CSU East Bay
60: Classification Accuracy of Unsupervised Learning Methods with Discrete and Mixture Distributed Indicators: a Monte Carlo Simulation Study
Chi Chang
61: Covariate-Adjusted Tensor Classification in High-Dimensions
Yuqing Pan, Florida State University; Qing Mai, Florida State University; Xin Zhang, Florida State University