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

Activity Details

434 Tue, 7/31/2018, 2:00 PM - 2:45 PM CC-West Hall B
SPEED: Classification and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science, SSC
Chair(s): Paul McNicholas, McMaster University
Oral Presentations for this session.
21: Targeted Maximum Likelihood Estimation of Causal Effects Based on Observing a Single Time Series
Ivana Malenica; Mark van der Laan, UC Berkeley
22: 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
23: Differentiable Approximations of Hidden Markov Models for Variational Bayesian Inference
Lun Yin, Duke Institute for Brain Sciences; John Pearson, Duke University
24: How to Effectively Communicate Misunderstood Statistical Terms
Hoiyi Ng, Amazon; Paavni Rattan, Amazon
25: 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
26: 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
27: 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
28: Classification via Product Conditional Density Estimates: Blending LDA and QDA
Jiae Kim; Steve MacEachern, The Ohio State University
29: 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
30: 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
31: Efficient Semiparametric Generalized Linear Models Based on Exponentially Tilted Splines
William H Aeberhard, Dalhousie University; Mark Hannay, Intrum Justitia CH
32: A Machine Learning (ML) Approach to Prognostic and Predictive Covariate Identification for Subgroup Analysis and Hypotheses Generation
David A James, Novartis
33: A Direct Approach to High-Dimensional Error-In-Variables Regression
Yunan Wu, University of Minnesota; Lan Wang, University of Minnesota
34: A Modified Approach to Component-Wise Gradient Boosting for High-Dimensional Regression Models
Brandon Butcher, University of Iowa; Brian J. Smith, University of Iowa
35: Efficient Big Data Model Selection with Applications to Fraud Detection
Gregory Vaughan, Bentley University
36: 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
37: 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
38: 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
39: Lookalike Audience Modeling
Sam Hawala, Resonate-Networks