Measuring Gentrification Over Time with the NYCHVS Robert Montgomery, NORC; Quentin Brummet, NORC; Nola du Toit, NORC at the University of Chicago; Peter Herman, NORC at the University of Chicago; Edward Mulrow, NORC at the University of Chicago
Findings from Analysis and Visualization of the New York City Housing and Vacancy Survey Data
Nels Grevstad, Metropolitan State University of Denver; Rachel Rosebrook, Metropolitan State University of Denver; Lance Barto, Metropolitan State University of Denver; Gil Leibovich, Metropolitan State University of Denver; Elizabeth Foster, Metropolitan State University of Denver; ThienNgo Le, Metropolitan State University of Denver; Kelsey Smith, Metropolitan State University of Denver; Nathanael Whitney, Metropolitan State University of Denver; Zoe Girkin, Metropolitan State University of Denver; Ahern Nelson, Metropolitan State University of Denver; Karan Bhargava, Metropolitan State University of Denver; Alex Whalen-Wagner, Metropolitan State University of Denver; Gemma Hoeppner, Metropolitan State University of Denver; Larry Breeden, Metropolitan State University of Denver; Ayako Zrust, Metropolitan State University of Denver; Travis Rebhan, Metropolitan State University of Denver; Anayeli Ochoa, Metropolitan State University of Denver
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Measuring Gentrification: a Data Driven Approach Steven Stier; Hend Aljobaily, University of Northern Colorado; Kofi Wagya, University of Northern Colorado; Michael Oduro-Safo, University of Northern Colorado
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Changes in Quality Housing Index in New York City Tuan Nguyen, University of Evansville; Mark Mozina, University of Evansville; Colton Albin, University of Evansville; Xianrui She, University of Evansville; Andrew Moore, University of Evansville
Essential Bayes: Paradigm, Techniques, and Applications (ADDED FEE) — Professional Development Continuing Education Course
ASA, Section for Statistical Programmers and Analysts
Instructor(s): Fang Chen, SAS Institute Inc; Amy Shi, SAS Institute Inc
This course reviews the fundamentals of Bayesian methods (prior distributions, inferences, multilevel modeling, and so on), introduces computational techniques (algorithms, convergence, and so on), and emphasizes the practical aspect of performing Bayesian analysis. It introduces the Bayesian treatment of various statistical topics, including regression models, multilevel hierarchical models, missing data analysis, model assessment, and predictions. Other commonly used Bayesian techniques, such as Monte Carlo simulation and use of historical information, are also presented. These techniques and Bayesian applications are illustrated through examples. SAS® software is used for analyses, including the MCMC procedure for general modeling and the specialized BGLIMM procedure for Bayesian generalized mixed models.
Attendees should have a background equivalent to an M.S. in applied statistics. Previous exposure to Bayesian methods and SAS software is useful. Familiarity with material at the level of the textbook Probability and Statistics, by DeGroot and Schervish (Addison Wesley), is appropriate.
Darius McDaniel, Emory Jesse Chittams, University of Pennsylvania Lillian Prince, Kent State University Mark Ward, Purdue University Renee Moore, Emory University
Causal Effect Estimation with Observational Data: Planning and Practice (ADDED FEE) — Professional Development Continuing Education Course
ASA, Section for Statistical Programmers and Analysts
Instructor(s): Michael Lamm, SAS Institute Inc; Clay Thompson, SAS Institute Inc
When does an effect estimate have a valid causal interpretation? Answering this question requires you to carefully evaluate if the estimation method used is appropriate given the data generating process. While these considerations are familiar when analyzing data from designed experiments, they are often ignored and can be much more challenging when analyzing observational data. This course introduces commonly used methods for estimating dichotomous treatment effects from observational data and tools for evaluating the conditions under which the effect estimate has a valid causal interpretation. In particular, for the estimation of treatment effects this course discusses the use of propensity score matching, inverse probability weighting, and doubly robust methods. For the evaluation of if a causal interpretation is valid for an estimated effect, this course reviews the role of directed graphs as a tool to represent the data generating process, reason about sources of association and bias, and construct a valid estimation strategy. From planning to analysis, these tools provide a rigorous and comprehensive workflow for causal effect estimation from observational data or data with imperfect randomization. This course provides a brief review of the theory behind these estimation and graphical methods and focuses on illustrating their application with a number of examples using some relatively new procedures in SAS/STAT® software. No prior experience with these estimation and graphical methods is assumed.
Predicting Lattice Reduction on Ideal Lattices (PeRIL) Bryan Ek, Space and Naval Warfare Systems Center Atlantic; Bryan Williams, Space and Naval Warfare Systems Center Atlantic; Emily Nystrom, Naval Information Warfare Center Atlantic; Jamie Lyle, Space and Naval Warfare Systems Center Atlantic; Peter Curry, Space and Naval Warfare Systems Center Atlantic; Scott Batson, Space and Naval Warfare Systems Center Atlantic
