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STATS 262: Intermediate Biostatistics: Regression, Prediction, Survival Analysis (HRP 262)

Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods, Cox regression, hazard ratios, time-dependent variables, longitudinal data structures, profile plots, missing data, modeling change, MANOVA, repeated-measures ANOVA, GEE, and mixed models. Emphasis is on practical applications. Prerequisites: basic ANOVA and linear regression.
Terms: Spr | Units: 3
Instructors: Sainani, K. (PI)

STATS 263: Design of Experiments (STATS 363)

Experiments vs observation. Confounding. Randomization. ANOVA.Blocking. Latin squares. Factorials and fractional factorials. Split plot. Response surfaces. Mixture designs. Optimal design. Central composite. Box-Behnken. Taguchi methods. Computer experiments and space filling designs. Prerequisites: probability at STATS 116 level or higher, and at least one course in linear models.
Terms: Win | Units: 3
Instructors: Owen, A. (PI) ; Lee, M. (TA) ; Loftus, J. (TA)

STATS 266: Advanced Statistical Methods for Observational Studies (CHPR 290, EDUC 260B)

Design principles and statistical methods for observational studies. Topics include: matching methods, sensitivity analysis, instrumental variables, graphical models, marginal structural models. 3 unit registration requires a small project and presentation. Computing is in R. Pre-requisites: HRP 261 and 262 or STATS 209 ( HRP 239), or equivalent. See http://rogosateaching.com/somgen290/
Terms: Spr | Units: 2-3
Instructors: Baiocchi, M. (PI) ; Rogosa, D. (PI)

STATS 267: Probability: Ten Great Ideas About Chance (PHIL 166, PHIL 266, STATS 167)

Foundational approaches to thinking about chance in matters such as gambling, the law, and everyday affairs. Topics include: chance and decisions; the mathematics of chance; frequencies, symmetry, and chance; Bayes great idea; chance and psychology; misuses of chance; and harnessing chance. Emphasis is on the philosophical underpinnings and problems. Prerequisite: exposure to probability or a first course in statistics at the level of STATS 60 or 116.
Terms: Spr | Units: 4
Instructors: Diaconis, P. (PI) ; Skyrms, B. (PI) ; Sepehri, A. (TA)

STATS 270: Bayesian Statistics I (STATS 370)

This is the first of a two course sequence on modern Bayesian statistics. Topics covered include: real world examples of large scale Bayesian analysis; basic tools (models, conjugate priors and their mixtures); Bayesian estimates, tests and credible intervals; foundations (axioms, exchangeability, likelihood principle); Bayesian computations (Gibbs sampler, data augmentation, etc.); prior specification. Prerequisites: statistics and probability at the level of Stats300A, Stats305, and Stats310.
Terms: Win | Units: 3

STATS 271: Bayesian Statistics II (STATS 371)

This is the second of a two course sequence on modern Bayesian statistics. Topics covered include: Asymptotic properties of Bayesian procedures and consistency (Doobs theorem, frequentists consistency, counter examples); connections between Bayesian methods and classical methods (the complete class theorem); generalization of exchangeability; general versions of the Bayes theorem in the undominated case; non parametric Bayesian methods (Dirichelet and Polya tree priors). Throughout general theory will be illustrated with classical examples. Prerequisites: Stats 270/370.
Terms: Spr | Units: 3
Instructors: Diaconis, P. (PI) ; Sabatti, C. (PI) ; Wong, W. (PI) ; Zhao, Q. (TA)

STATS 290: Paradigms for Computing with Data

Advanced programming and computing techniques to support projects in data analysis and related research. For Statistics graduate students and others whose research involves data analysis and development of associated computational software. Prerequisites: Programming experience including familiarity with R; computing at least at the level of CS 106; statistics at the level of STATS 110 or 141.
Terms: Win | Units: 3

STATS 298: Industrial Research for Statisticians

Masters-level research as in 299, but with the approval and supervision of a faculty adviser, it must be conducted for an off-campus employer. Students must submit a written final report upon completion of the internship in order to receive credit. Repeatable for credit. Prerequisite: enrollment in Statistics M.S. program.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable 2 times (up to 2 units total)

STATS 299: Independent Study

For Statistics M.S. students only. Reading or research program under the supervision of a Statistics faculty member. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit

STATS 300: Advanced Topics in Statistics: Stochastic Block Models and Latent Variable Models

Main topic: statistical inference of latent variable models (including SBM), using EM-like algorithms. The critical step is the determination of the conditional distribution of the latent variables given the observed data, which is doable for mixture models and hidden Markov models. For more complex models such as the stochastic block model (SBM: popular in sociology, physics, biology, etc.) variational approximations can be used to derive a generalized version of EM algorithm. This approach can be extended to Bayesian inference (variational Bayes EM algorithm). If time permits, change-point detection models will be introduced. Topics will be illustrated with examples from genomics.
Terms: Sum | Units: 2-3 | Repeatable for credit
Instructors: Robin, S. (PI) ; Sepehri, A. (PI)
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