STATS 300C: Theory of Statistics
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: 24

Grading: Letter or Credit/No Credit
Instructors:
Candes, E. (PI)
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

Grading: Satisfactory/No Credit
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 for credit

Grading: Satisfactory/No Credit
Instructors:
Holmes, S. (PI)
STATS 305: Introduction to Statistical Modeling
Review of univariate regression. Multiple regression. Geometry, subspaces, orthogonality, projections, normal equations, rank deficiency, estimable functions and GaussMarkov theorem. Computation via QR decomposition, GrammSchmidt 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.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Tibshirani, R. (PI)
;
Achanta, R. (TA)
;
Markovic, J. (TA)
;
Wang, J. (TA)
...
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Instructors:
Tibshirani, R. (PI)
;
Achanta, R. (TA)
;
Markovic, J. (TA)
;
Wang, J. (TA)
;
YANG, J. (TA)
STATS 306A: Methods for Applied Statistics
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: 305 or equivalent.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Owen, A. (PI)
STATS 306B: Methods for Applied Statistics: Empirical Bayes Methods
Empirical Bayes procedures for estimation, testing, and prediction, especially as applied to largescale problems.
Terms: Spr

Units: 23

Grading: Letter or Credit/No Credit
Instructors:
Efron, B. (PI)
STATS 310A: Theory of Probability (MATH 230A)
Mathematical tools: sigma algebras, measure theory, connections between coin tossing and Lebesgue measure, basic convergence theorems. Probability: independence, BorelCantelli lemmas, almost sure and Lp convergence, weak and strong laws of large numbers. Large deviations. Weak convergence; central limit theorems; Poisson convergence; Stein's method. Prerequisites: 116,
MATH 171.
Terms: Aut

Units: 24

Grading: Letter or Credit/No Credit
STATS 310B: Theory of Probability (MATH 230B)
Conditional expectations, discrete time martingales, stopping times, uniform integrability, applications to 01 laws, RadonNikodym Theorem, ruin problems, etc. Other topics as time allows selected from (i) local limit theorems, (ii) renewal theory, (iii) discrete time Markov chains, (iv) random walk theory,nn(v) ergodic theory. Prerequisite: 310A or
MATH 230A.
Terms: Win

Units: 23

Grading: Letter or Credit/No Credit
Instructors:
Chatterjee, S. (PI)
STATS 310C: Theory of Probability (MATH 230C)
Continuous time stochastic processes: martingales, Brownian motion, stationary independent increments, Markov jump processes and Gaussian processes. Invariance principle, random walks, LIL and functional CLT. Markov and strong Markov property. Infinitely divisible laws. Some ergodic theory. Prerequisite: 310B or
MATH 230B.
Terms: Spr

Units: 24

Grading: Letter or Credit/No Credit
Instructors:
Diaconis, P. (PI)
STATS 311: Information Theory and Statistics (EE 377)
Information theoretic techniques in probability and statistics. Fano, Assouad,nand Le Cam methods for optimality guarantees in estimation. Large deviationsnand concentration inequalities (Sanov's theorem, hypothesis testing, thenentropy method, concentration of measure). Approximation of (Bayes) optimalnprocedures, surrogate risks, fdivergences. Penalized estimators and minimumndescription length. Online game playing, gambling, noregret learning. Prerequisites:
EE 376A (or equivalent) or
STATS 300A.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Duchi, J. (PI)
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