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21 - 30 of 39 results for: STATS

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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 300B: Theory of Statistics

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
Instructors: Siegmund, D. (PI) ; SUR, P. (TA) ; Zhao, Q. (TA)

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 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
Instructors: Owen, A. (PI) ; Qian, J. (TA) ; YANG, J. (TA)

STATS 310B: Theory of Probability (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,nn(v) ergodic theory. Prerequisite: 310A or MATH 230A.
Terms: Win | Units: 2-3
Instructors: Chatterjee, S. (PI) ; Bai, Y. (TA) ; Huang, R. (TA)

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, f-divergences. Penalized estimators and minimumndescription length. Online game playing, gambling, no-regret learning. Prerequisites: EE 376A (or equivalent) or STATS 300A.
Terms: Win | Units: 3
Instructors: Duchi, J. (PI) ; Namkoong, H. (TA)

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 305, 306A,B or consent of instructor.
Terms: Win | Units: 2-3

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: 1-3 | Repeatable for credit
Instructors: Montanari, A. (PI) ; Romano, J. (PI) ; Wong, W. (PI)

STATS 360: Advanced Statistical Methods for Earth System Analysis (ESS 260)

Introduction for graduate students to important issues in data analysis relevant to earth system studies. Emphasis on methodology, concepts and implementation (in R), rather than formal proofs. Likely topics include the bootstrap, non-parametric methods, regression in the presence of spatial and temporal correlation, extreme value analysis, time-series analysis, high-dimensional regressions and change-point models. Topics subject to change each year. Prerequisites: STATS 110 or equivalent.
Terms: Win | Units: 3
Instructors: Rajaratnam, B. (PI)
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