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
Instructors:
Baiocchi, M. (PI)
;
Candes, E. (PI)
;
Chatterjee, S. (PI)
...
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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)
;
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: R. A. Fisher and 20th Century Statistics
An introduction to statistical inference and practice through the eyes of its greatest exponent, R. A. Fisher. Each class one of Fisher's papers will be discussed, together with one or more commentaries or extensions by later statisticians. Topics to be covered will include: Fisher's life; the Student t-test, the 1915 paper on the correlation coefficient, the degrees-of-freedom controversy for chi-squared, the 1922 and 1925 papers on statistical estimation, Statistical Methods for Research Workers (1925), conditional inference, the Design of Experiments (1935), the dispute with Neyman, discriminant analysis, the sampling of species, dispersion on a sphere, fiducial inference, and Statistical Methods and Scientific Inference (1956).
Terms: Sum
| Units: 2-3
| Repeatable
for credit
Instructors:
Zabell, S. (PI)
;
Panigrahi, S. (TA)
STATS 300A: Theory of Statistics I
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: 2-3
STATS 300B: Theory of Statistics II
Elementary decision theory; loss and risk functions, Bayes estimation; UMVU estimator, minimax estimators, shrinkage estimators. Hypothesis testing and confidence intervals: Neyman-Pearson 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: 2-4
STATS 300C: Theory of Statistics III
Decision theory formulation of statistical problems. Minimax, admissible procedures. Complete class theorems ("all" minimax or admissible procedures are "Bayes"), Bayes procedures, conjugate priors, hierarchical models. Bayesian non parametrics: diaichlet, tail free, polya trees, bayesian sieves. Inconsistency of bayes rules.
Terms: Spr
| Units: 2-4
STATS 302: Qualifying Exams Workshop
Prepares Statistics Ph.D. students for the qualifying exams by reviewing relevant course topics and problem solving strategies.
Terms: Sum
| Units: 3
STATS 303: PhD First Year Student Workshop
For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.
Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
4 times
(up to 4 units total)
Instructors:
Candes, E. (PI)
;
Holmes, S. (PI)
STATS 305A: Introduction to Statistical Modeling
Review of univariate regression. Multiple regression. Geometry, subspaces, orthogonality, projections, normal equations, rank deficiency, estimable functions and Gauss-Markov theorem. Computation via QR decomposition, Gramm-Schmidt orthogonalization and the SVD. Interpreting coefficients, collinearity, graphical displays. Fits and the Hat matrix, leverage & influence, diagnostics, weighted least squares and resistance. Model selection, Cp/Aic and crossvalidation, stepwise, lasso. Basis expansions, splines. Multivariate normal distribution theory. ANOVA: Sources of measurements, fixed and random effects, randomization. Emphasis on problem sets involving substantive computations with data sets. Prerequisites: consent of instructor, 116, 200, applied statistics course,
CS 106A,
MATH 114. (NB: prior to 2016-17 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Aut
| Units: 3
Instructors:
Owen, A. (PI)
;
Guan, L. (TA)
;
Le, Y. (TA)
;
Markovic, J. (TA)
;
Powers, S. (TA)
;
Rosenman, E. (TA)
STATS 305B: Methods for Applied Statistics I
Regression modeling extended to categorical data. Logistic regression. Loglinear models. Generalized linear models. Discriminant analysis. Categorical data models from information retrieval and Internet modeling. Prerequisite: 305A or equivalent. (NB: prior to 2016-17 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Win
| Units: 3
STATS 305C: Methods for Applied Statistics II: Applied Bayesian Statistics
Applied Bayesian statistics. Fundamentals, hierarchical models, computing. (NB: prior to 2016-17 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Spr
| Units: 3
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