Results for STATS

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.

Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites: MATH 52 and familiarity with infinite series, or equivalent.

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.

For students majoring in Mathematical and Computational Science only. Students obtain employment in a relevant industrial or research activity to enhance their professional experience.

For undergraduates.

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Pre- or corequisite: 200.

Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and state-space models. Seasonality, transformations, and introduction to financial time series. Prerequisite: basic course in Statistics at the level of 200.

The bootstrap is a computer-based method for assigning measures of accuracy to statistical estimates. By substituting computation in place of mathematical formulas, it permits the statistical analysis of complicated estimators. Topics: nonparametric assessment of standard errors, biases, and confidence intervals; related resampling methods including the jackknife, cross-validation, and permutation tests. Theory and applications. Prerequisite: course in statistics or probability.

Renewal theory, Brownian motion, Gaussian processes, second order processes, martingales.

Topics of current interest. May be repeated for credit.

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.

Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods, Cox regression, hazard ratios, time-dependent variables, longitudinal data structures, profile plots, missing data, modeling change, MANOVA, repeated-measures ANOVA, GEE, and mixed models. Emphasis is on practical applications. Prerequisites: basic ANOVA and linear regression.

For students in the M.S. program in Financial Mathematics only. Students obtain employment, with the approval and supervision of a faculty member, in a relevant industrial or research activity to enhance their professional experience. Students must submit a written final report upon completion of the internship in order to receive credit. May be repeated once for credit. Prerequisite: consent of adviser.

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. May be repeated once for credit. Prerequisite: enrollment in Statistics M.S. or Ph.D. program, prior to candidacy.

For Statistics M.S. students only. Reading or research program under the supervision of a Statistics faculty member. May be repeated for credit.

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.

For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.

Unsupervised learning techniques in statistics, machine learning, and data mining.

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.

Two-part sequence. New techniques for predictive and descriptive learning using ideas that bridge gaps among statistics, computer science, and artificial intelligence. Emphasis is on statistical aspects of their application and integration with more standard statistical methodology. Predictive learning refers to estimating models from data with the goal of predicting future outcomes, in particular, regression and classification models. Descriptive learning is used to discover general patterns and relationships in data without a predictive goal, viewed from a statistical perspective as computer automated exploratory analysis of large complex data sets.

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.

Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit.

Statistical methods for analyzing human genetics studies of Mendelian disorders and common complex traits. Probable topics include: principles of population genetics; epidemiologic designs; familial aggregation; segregation analysis; linkage analysis; linkage-disequilibrium-based association mapping approaches; and genome-wide analysis based on high-throughput genotyping platforms. Prerequisite: STATS 116 or equivalent or consent of instructor.

Concentration of measure techniques. Mean field models for disordered systems: infinite size limit, computing the free energy, ultrametricity, dynamics. Interpolation techniques and infinite size limit in information theory and coding. May be repeated once for credit. Prerequisite: 310A or equivalent.

Selected material about probability on trees and networks, random walk in random and non-random environments, percolation and related interacting particle systems. Prerequisite: Exposure to measure theoretic probability and to stochastic processes.

Bayesian analysis of infinite-dimensional models including the Dirichlet Process, species sampling models, neutral to the right processes, completely random measures, Gaussian Processes, and the Polya tree. Review of hierarchical constructions, inference algorithms, posterior asymptotics, and successful applications in the literature. Prerequisites: Stats 310A and basic understanding of Bayesian analysis at the level of Stats 270.

Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's drop-in consulting service, analyze client data, and prepare formal written reports. Seminar provides supervised experience in short term consulting. May be repeated for credit. Prerequisites: course work in applied statistics or data analysis, and consent of instructor.

Applications of Computational Statistics and Data Mining to Biological Data. Attendance mandatory. Instructor approval required.

For Statistics PhD students defending their dissertation.

Doctoral research as in 298, but must be conducted for an off-campus employer. Final report required. May be repeated for credit. Prerequisite: Statistics Ph.D. candidate.

Research work as distinguished from independent study of nonresearch character listed in 199. May be repeated for credit.

(Same as LAW 564). This interdisciplinary course will survey the current landscape of the global markets as the world continues to progress through the financial crisis. We will discuss the sweeping change underway at the policy level by regulators and legislators around the world as well as the strategic discussions, which will include guest-lecturer perspectives on how affairs may change as a result and where the greatest opportunities exist for students entering the world of finance today. The course will also review, in a non-technical way, the basics of the financial derivatives and other quantitative techniques that are a core part of the global capital markets. The subject matter, by necessity, is multi-disciplinary and the course is particularly suited to those students having an interest in finance-based careers, entering legal, regulatory or public policy positions related to finance or studying the evolution of modern financial markets. Elements used in grading: Class Participation, Attendance, Final Paper. Consent Application: To apply for this course, students must complete and e-mail the Consent Application Form available on the SLS Registrar's Office website (see Registration and Selection of Classes for Stanford Law Students) to the instructors. See Consent Application Form for submission deadline.