Research Interests: applications of statistics to public health, design and analysis of experiments and observational studies for comparing treatments, longitudinal data, measurement error, medicine and economics
Links: Personal Website
PhD, Stanford University, 2002
BA, Harvard University, 1997
For more information, go to My Personal Page
Jennifer A. Faerber, Marshall M. Joffe, Dylan Small, Rongmei Zhang, Gregory K. Brown, Thomas R. Ten Have (2018), A Simple Model Allowing Modification of the Effect of a Randomized Intervention by Post-Randomization Variables, Journal of Causal Inference, 5 (2).
Ashkan Ertefaie, Dylan Small, Paul R. Rosenbaum (2018), Quantitative evaluation of the trade-off of strengthened instruments and sample size in observational studies, Journal of the American Statistical Association.
Kwonsang Lee, Dylan Small, Jesse Y. Hsu, Jeffrey H. Silber, Paul R. Rosenbaum (2018), Discovering effect modification in an observational study of surgical mortality at hospitals with superior nursing, Journal of the Royal Statistical Society, Series A.
Hyunseung Kang, Benno Kreuels, Jurgen May, Dylan Small (2017), Full Matching Approach to Instrumental Variables Estimation with Application to the Effect of Malaria on Stunting, The Annals of Applied Statistics, 10 (1), pp. 335-364.
Raiden Hasegawa and Dylan Small (2017), Sensitivity Analysis for Matched Pair Analysis of Binary Data: From Worst Case to Average Case Analysis, Biometrics, 73 (4), pp. 1424-1432.
Dylan Small, Zhiqiang Tan, Roland R. Ramsahai, Scott A. Lorch, M. Alan Brookhart (2017), Instrumental Variable Estimation with a Stochastic Monotonicity Assumption, Statistical Science, 32 (4), pp. 561-579.
Mitesh S. Patel, Emelia J. Benjamin, Kevin G. Volpp, Caroline S. Fox, Dylan Small, Joseph M. Massaro, Jane J. Lee, Victoria Hilbert, Maureen Valentino, Devon H. Taylor, Emily S. Manders, Karen Mutalik, Jingsan Zhu, Wenli Wang, Joanne M. Murabito (2017), Effect of a Game-Based Intervention Designed to Enhance Social Incentives to Increase Physical Activity Among Families: The BE FIT Randomized Clinical Trial, JAMA Internal Medicine, 177 (11), pp. 1586-1593.
Stephen Burgess, Dylan Small, Simon G Thompson (2017), A Review of Instrumental Variable Estimators for Mendelian Randomization, Statistical Methods in Medical Research, 26 (5), pp. 2333-2355.
Edward Kennedy, Zongming Ma, Matthew McHugh, Dylan Small (2017), Nonparametric methods for doubly robust estimation of continuous treatment effects, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79 (4), pp. 1229-1245.
Data summaries and descriptive statistics; introduction to a statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus, but is not inappropriate for students in the college.
Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.
Further development of the material in STAT 111, in particular the analysis of variance, multiple regression, non-parametric procedures and the analysis of categorical data. Data analysis via statistical packages.
This course will cover the design and analysis of sample surveys. Topics include simple sampling, stratified sampling, cluster sampling, graphics, regression analysis using complex surveys and methods for handling nonresponse bias.
Elements of matrix algebra. Discrete and continuous random variables and their distributions. Moments and moment generating functions. Joint distributions. Functions and transformations of random variables. Law of large numbers and the central limit theorem. Point estimation: sufficiency, maximum likelihood, minimum variance. Confidence intervals.
An introduction to the mathematical theory of statistics. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. Hypothesis testing, with a focus on likelihood ratio tests and the consequent development of "t" tests and hypothesis tests in regression and ANOVA. Nonparametric procedures.
This is a course in econometrics for graduate students. The goal is to prepare students for empirical research by studying econometric methodology and its theoretical foundations. Students taking the course should be familiar with elementary statistical methodology and basic linear algebra, and should have some programming experience. Topics include conditional expectation and linear projection, asymptotic statistical theory, ordinary least squares estimation, the bootstrap and jackknife, instrumental variables and two-stage least squares, specification tests, systems of equations, generalized least squares, and introduction to use of linear panel data models.
Topics include system estimation with instrumental variables, fixed effects and random effects estimation, M-estimation, nonlinear regression, quantile regression, maximum likelihood estimation, generalized method of moments estimation, minimum distance estimation, and binary and multinomial response models. Both theory and applications will be stressed.
This course will cover the design and analysis of sample surveys. Topics include simple random sampling, stratified sampling, cluster sampling, graphics, regression analysis using complex surveys and methods for handling nonresponse bias.
This course will cover statistical methods for the design and analysis of observational studies. Topics will include the potential outcomes framework for causal inference; randomized experiments; matching and propensity score methods for controlling confounding in observational studies; tests of hidden bias; sensitivity analysis; and instrumental variables.
This course is designed for Ph.D. students in statistics and will cover various advanced methods and models that are useful in applied statistics. Topics for the course will include missing data, measurement error, nonlinear and generalized linear regression models, survival analysis, experimental design, longitudinal studies, building R packages and reproducible research.
Decision theory and statistical optimality criteria, sufficiency, point estimation and hypothesis testing methods and theory.
Theory of the Gaussian Linear Model, with applications to illustrate and complement the theory. Distribution theory of standard tests and estimates in multiple regression and ANOVA models. Model selection and its consequences. Random effects, Bayes, empirical Bayes and minimax estimation for such models. Generalized (Log-linear) models for specific non-Gaussian settings.
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.
New Wharton research examines the long-term impact of playing high school or college football.Knowledge @ Wharton - 2017/07/21