Legend:
CC = Vancouver Convention Centre
F = Fairmont Waterfront Vancouver
* = applied session ! = JSM meeting theme
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434
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Tue, 7/31/2018,
2:00 PM -
2:45 PM
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CC-West Hall B
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SPEED: Classification and Data Science — Contributed Poster Presentations
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Section on Statistical Learning and Data Science, SSC
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Chair(s): Paul McNicholas, McMaster University
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Oral Presentations
for this session.
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21:
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Targeted Maximum Likelihood Estimation of Causal Effects Based on Observing a Single Time Series
Ivana Malenica; Mark van der Laan, UC Berkeley
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22:
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Accessible Statistical Reports in R: Using R, Markdown, and Word to Create Accessible Reproducible Documents
Robert Montgomery, NORC; Peter Herman, NORC at the University of Chicago; Qiao Ma, NORC at the University of Chicago; Stephen Schacht, NORC at the University of Chicago
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23:
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Differentiable Approximations of Hidden Markov Models for Variational Bayesian Inference
Lun Yin, Duke Institute for Brain Sciences; John Pearson, Duke University
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24:
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How to Effectively Communicate Misunderstood Statistical Terms
Hoiyi Ng, Amazon; Paavni Rattan, Amazon
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25:
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Aggregated Pairwise Classification of Statistical Shapes with Optimal Points of Projection
Min Ho Cho, The Ohio State University; Sebastian Kurtek, The Ohio State University; Steve MacEachern, The Ohio State University
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26:
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Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes Under Possible Non-Proportional Hazards
Lauren Spirko, Temple University; Karthik Devarajan, Fox Chase Cancer Center
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27:
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Improving a Predictive Model of Student Progress in an Online Course by Adding Learned Features from Unstructured Text Data
Huafeng Zhang, The Refugee Center Online
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28:
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Classification via Product Conditional Density Estimates: Blending LDA and QDA
Jiae Kim; Steve MacEachern, The Ohio State University
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29:
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Comparison of Missing Data Methods in the Use of LASSO Regression for Model Selection with Applications to the National Trauma Data Bank
Sarah B Peskoe, Duke University; Tracy Truong, Duke University; Lily R Mundy, Duke University School of Medicine; Ronnie L Shammas, Duke University School of Medicine; Scott T Hollenbeck, Duke University School of Medicine
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30:
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An Alternative to the Carnegie Classifications: Using Structural Equation Models to Identify Similar Doctoral Institutions
Paul Harmon, Montana State University; Sarah McKnight, Montana State University; Laura Hildreth, Montana State University; Ian C. Godwin, Montana State University Office of Planning and Analysis; Mark Greenwood, Montana State University
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31:
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Efficient Semiparametric Generalized Linear Models Based on Exponentially Tilted Splines
William H Aeberhard, Dalhousie University; Mark Hannay, Intrum Justitia CH
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32:
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A Machine Learning (ML) Approach to Prognostic and Predictive Covariate Identification for Subgroup Analysis and Hypotheses Generation
David A James, Novartis
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33:
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A Direct Approach to High-Dimensional Error-In-Variables Regression
Yunan Wu, University of Minnesota; Lan Wang, University of Minnesota
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34:
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A Modified Approach to Component-Wise Gradient Boosting for High-Dimensional Regression Models
Brandon Butcher, University of Iowa; Brian J. Smith, University of Iowa
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35:
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Efficient Big Data Model Selection with Applications to Fraud Detection
Gregory Vaughan, Bentley University
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36:
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Predicting Overflow: A Novel Application of Latrine Sensors and Machine Learning for Optimizing Sanitation Services in Informal Settlements
Phillip Turman-Bryant, Portland State University; Evan Thomas, Portland State University
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37:
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Undergraduate Data Science Statistics Pathways: What Is Needed for Entry into the Major?
Rebecca Hartzler, Charles A. Dana Center, University of Texas at Austin; Nicholas J. Horton, Amherst College
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38:
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Assessing Divide-and-Conquer Latent Class Analysis
Qiao Ma, NORC at the University of Chicago; Meimeizi Zhu, NORC at the University of Chicago; Edward Mulrow, NORC at the University of Chicago
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39:
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Lookalike Audience Modeling
Sam Hawala, Resonate-Networks
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