printer friendly pageSTATS 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. IMPORTANT: F-1 international students enrolled in this CPT course cannot start working without first obtaining a CPT-endorsed I-20 from Bechtel International Center (enrolling in the CPT course alone is insufficient to meet federal immigration regulations).
Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
3 times
(up to 3 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: 1-5
| Repeatable
for credit
Instructors:
Baiocchi, M. (PI)
;
Candes, E. (PI)
;
Chatterjee, S. (PI)
...
more instructors for STATS 299 »
Instructors:
Baiocchi, M. (PI)
;
Candes, E. (PI)
;
Chatterjee, S. (PI)
;
Duchi, J. (PI)
;
Hastie, T. (PI)
;
Johnstone, I. (PI)
;
Liang, P. (PI)
;
Linderman, S. (PI)
;
Narasimhan, B. (PI)
;
Romano, J. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
;
Tian, L. (PI)
;
Tibshirani, R. (PI)
;
Wager, S. (PI)
;
Walther, G. (PI)
;
Wong, W. (PI)
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: 3
STATS 305B: Applied Statistics II
This course uses exponential family structure to motivate generalized linear models and other useful applied techniques including survival analysis methods and Bayes and empirical Bayes analyses. The lectures are based on a forthcoming book whose notes will be distributed. Prerequisites: 305A or consent of the instructor.
Terms: Win
| Units: 3
STATS 310B: Theory of Probability II (MATH 230B)
Conditional expectations, discrete time martingales, stopping times, uniform integrability, applications to 0-1 laws, Radon-Nikodym 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,n(v) ergodic theory.
http://statweb.stanford.edu/~adembo/stat-310b. Prerequisite: 310A or
MATH 230A.
Terms: Win
| Units: 3
STATS 315A: Modern Applied Statistics: Learning
Overview of supervised learning. Linear regression and related methods. Model selection, least angle regression and the lasso, stepwise methods. Classification. Linear discriminant analysis, logistic regression, and support vector machines (SVMs). Basis expansions, splines and regularization. Kernel methods. Generalized additive models. Kernel smoothing. Gaussian mixtures and the EM algorithm. Model assessment and selection: crossvalidation and the bootstrap. Pathwise coordinate descent. Sparse graphical models. Prerequisites:
STATS 305A, 305B, 305C or consent of instructor.
Terms: Win
| Units: 3
STATS 317: Stochastic Processes
Semimartingales, stochastic integration, Ito's formula, Girsanov's theorem. Gaussian and related processes. Stationary/isotropic processes. Integral geometry and geometric probability. Maxima of random fields and applications to spatial statistics and imaging.
Terms: Win
| Units: 3
Instructors:
Li, S. (PI)
STATS 318: Modern Markov Chains (MATH 235)
Tools for understanding Markov chains as they arise in applications. Random walk on graphs, reversible Markov chains, Metropolis algorithm, Gibbs sampler, hybrid Monte Carlo, auxiliary variables, hit and run, Swedson-Wong algorithms, geometric theory, Poincare-Nash-Cheeger-Log-Sobolov inequalities. Comparison techniques, coupling, stationary times, Harris recurrence, central limit theorems, and large deviations.
Terms: Win
| Units: 3
STATS 319: Literature of Statistics
Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
STATS 362: Topic: Monte Carlo
Random numbers and vectors: inversion, acceptance-rejection, copulas. Variance reduction: antithetics, stratification, control variates, importance sampling. MCMC: Markov chains, detailed balance, Metropolis-Hastings, random walk Metropolis,nnindependence sampler, Gibbs sampling, slice sampler, hybrids of Gibbs and Metropolis, tempering. Sequential Monte Carlo. Quasi-Monte Carlo. Randomized quasi-Monte Carlo. Examples, problems and motivation from Bayesian statistics,nnmachine learning, computational finance and graphics. May be repeat for credit.
Terms: Win
| Units: 3
Instructors:
Owen, A. (PI)
;
Pan, Z. (TA)
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