Legend:
CC = Vancouver Convention Centre
F = Fairmont Waterfront Vancouver
* = applied session ! = JSM meeting theme
341
Tue, 7/31/2018,
10:30 AM -
12:20 PM
CC-West 212
SPEED: Classification and Data Science — Contributed Speed
Section on Statistical Learning and Data Science , SSC
Chair(s): Jesse Cambon, Office of Immigration Statistics
Poster Presentations
for this session.
10:35 AM
Targeted Maximum Likelihood Estimation of Causal Effects Based on Observing a Single Time Series
Ivana Malenica ; Mark van der Laan, UC Berkeley
10:40 AM
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
10:45 AM
Differentiable Approximations of Hidden Markov Models for Variational Bayesian Inference
Lun Yin, Duke Institute for Brain Sciences ; John Pearson, Duke University
10:50 AM
How to Effectively Communicate Misunderstood Statistical Terms
Hoiyi Ng, Amazon ; Paavni Rattan, Amazon
10:55 AM
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
11:00 AM
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
11:05 AM
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
11:10 AM
Classification via Product Conditional Density Estimates: Blending LDA and QDA
Jiae Kim ; Steve MacEachern, The Ohio State University
11:15 AM
Comparison of Missing Data Methods in the Use of LASSO Regression for Model Selection with Applications to the National Trauma Data Bank
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
11:20 AM
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
11:30 AM
Efficient Semiparametric Generalized Linear Models Based on Exponentially Tilted Splines
William H Aeberhard, Dalhousie University ; Mark Hannay, Intrum Justitia CH
11:35 AM
A Machine Learning (ML) Approach to Prognostic and Predictive Covariate Identification for Subgroup Analysis and Hypotheses Generation
David A James, Novartis
11:40 AM
A Direct Approach to High-Dimensional Error-In-Variables Regression
Yunan Wu, University of Minnesota ; Lan Wang, University of Minnesota
11:45 AM
A Modified Approach to Component-Wise Gradient Boosting for High-Dimensional Regression Models
Brandon Butcher, University of Iowa ; Brian J. Smith, University of Iowa
11:50 AM
Efficient Big Data Model Selection with Applications to Fraud Detection
Gregory Vaughan, Bentley University
11:55 AM
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
12:00 PM
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
12:05 PM
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
12:10 PM
Lookalike Audience Modeling
Sam Hawala, Resonate-Networks
