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. Prerequisites:
HRP 261 and 262 or STAT 209 (
HRP 239), or equivalent. See
http://rogosateaching.com/somgen290/
Terms: Spr

Units: 23

Grading: Medical Option (MedLtrCR/NC)
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

Grading: Letter or Credit/No Credit
Instructors:
Diaconis, P. (PI)
;
Skyrms, B. (PI)
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

Grading: Letter or Credit/No Credit
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

Grading: Letter or Credit/No Credit
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

Grading: Letter or Credit/No Credit
Instructors:
Chambers, J. (PI)
;
Narasimhan, B. (PI)
STATS 298: Industrial Research for Statisticians
Masterslevel research as in 299, but with the approval and supervision of a faculty adviser, it must be conducted for an offcampus 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

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Candes, E. (PI)
;
Dembo, A. (PI)
;
Diaconis, P. (PI)
;
Donoho, D. (PI)
...
more instructors for STATS 298 »
Instructors:
Candes, E. (PI)
;
Dembo, A. (PI)
;
Diaconis, P. (PI)
;
Donoho, D. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
;
Friedman, J. (PI)
;
Hastie, T. (PI)
;
Holmes, S. (PI)
;
Ioannidis, J. (PI)
;
Johnstone, I. (PI)
;
Lai, T. (PI)
;
Montanari, A. (PI)
;
Olkin, I. (PI)
;
Olshen, R. (PI)
;
Owen, A. (PI)
;
Rajaratnam, B. (PI)
;
Romano, J. (PI)
;
Sabatti, C. (PI)
;
Siegmund, D. (PI)
;
Switzer, P. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (PI)
;
Walther, G. (PI)
;
Wong, W. (PI)
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: 110

Repeatable for credit

Grading: Letter or Credit/No Credit
Instructors:
Baiocchi, M. (PI)
;
Candes, E. (PI)
;
Chatterjee, S. (PI)
;
Dembo, A. (PI)
...
more instructors for STATS 299 »
Instructors:
Baiocchi, M. (PI)
;
Candes, E. (PI)
;
Chatterjee, S. (PI)
;
Dembo, A. (PI)
;
Diaconis, P. (PI)
;
Donoho, D. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
;
Friedman, J. (PI)
;
Hastie, T. (PI)
;
Holmes, S. (PI)
;
Johnstone, I. (PI)
;
Khare, A. (PI)
;
Lai, T. (PI)
;
Mackey, L. (PI)
;
Montanari, A. (PI)
;
Mukherjee, R. (PI)
;
Olkin, I. (PI)
;
Olshen, R. (PI)
;
Owen, A. (PI)
;
Rajaratnam, B. (PI)
;
Rogosa, D. (PI)
;
Romano, J. (PI)
;
Sabatti, C. (PI)
;
Siegmund, D. (PI)
;
Switzer, P. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (PI)
;
Walther, G. (PI)
;
Wong, W. (PI)
STATS 300: Advanced Topics in Statistics
Topic: Exploratory Multivariate Data Analysis. Describing and visualizing data with principal component analysis (PCA) for continuous data, correspondence analysis (CA) for contingency tables, multiple correspondence analysis (MCA) for categorical data, factorial analysis for mixed data (FAMD) for both continuous and categorical data, and multiple factor analysis (MFA) for data structured into groups of variables. Studying and visualization of the correlation between groups of variables with the RV coefficient. Performing PCA with missing values, matrix completion of continuous and categorical data with principal components. Examples from sensory analysis, public health, genetics. All the analysis will be performed with R.
Terms: Sum

Units: 23

Repeatable for credit

Grading: Letter or Credit/No Credit
STATS 300A: Theory of Statistics
Finite sample optimality of statistical procedures; Decision theory: loss, risk, admissibility; Principles of data reduction: sufficiency, ancillarity, completeness; Statistical models: exponential families, group families, nonparametric families; Point estimation: optimal unbiased and equivariant estimation, Bayes estimation, minimax estimation; Hypothesis testing and confidence intervals: uniformly most powerful tests, uniformly most accurate confidence intervals, optimal unbiased and invariant tests. Prerequisites: Real analysis, introductory probability (at the level of
STATS 116), and introductory statistics.
Terms: Aut

Units: 23

Grading: Letter or Credit/No Credit
Instructors:
Mackey, L. (PI)
;
Guu, K. (TA)
;
Ruan, F. (TA)
;
Zhao, Q. (TA)
...
more instructors for STATS 300A »
STATS 300B: Theory of Statistics
Elementary decision theory; loss and risk functions, Bayes estimation; UMVU estimator, minimax estimators, shrinkage estimators. Hypothesis testing and confidence intervals: NeymanPearson theory; UMP tests and uniformly most accurate confidence intervals; use of unbiasedness and invariance to eliminate nuisance parameters. Large sample theory: basic convergence concepts; robustness; efficiency; contiguity, locally asymptotically normal experiments; convolution theorem; asymptotically UMP and maximin tests. Asymptotic theory of likelihood ratio and score tests. Rank permutation and randomization tests; jackknife, bootstrap, subsampling and other resampling methods. Further topics: sequential analysis, optimal experimental design, empirical processes with applications to statistics, Edgeworth expansions, density estimation, time series.
Terms: Win

Units: 24

Grading: Letter or Credit/No Credit
Instructors:
Siegmund, D. (PI)
Filter Results: