The first half of the course consists of an accelerated introduction to the Python programming language, including brief introductions to object-oriented and functional programming styles as well as tools for code optimization. It starts with a brief review of necessary probability concepts such as types of convergence and background on exponential families and related topics. Topics include: (1) classification and machine learning, including support vector machines, recursive partitioning, and ensemble methods; (2) methods for analyzing sets of curves, surfaces and images, including functional data analysis, wavelets, independent component analysis, and random field models; (3) modern regression, including splines and generalized additive models, (4) methods for analyzing structured dependent data, including mixed effects models, hierarchical models, graphical models, and Bayesian networks; and (5) clustering, detection, and dimension reduction methods, including manifold learning, spectral clustering, and bump hunting. Topics covered include measure and probability spaces, random variables, independence, expectation, convergence, laws of large numbers, convergence in distribution, central limit theorems, conditional expectation and martingales. Pre-requisite: Graduate standing and STATS 400 (or equivalent) or permission of instructor. (4 Credits), Statistics 604: Statistical Investigations in a Consulting Framework, Statistical Investigations in a Consulting Framework --- This course provides graduate students with a variety of modeling and data analysis opportunities through consultations with other researchers. Topics: Data acquisition; databases; low level processing; normalization; quality control; statistical inference (group comparisons, cyclicity, survival); multiple comparisons; statistical learning algorithms; clustering; visualization; and case studies. It then introduces students to measure theory and integration. The course covers a number of advanced modeling techniques, both classical and modern, which belong to the class of hierarchical models, spatiotemporal models, dynamics models and Bayesian nonparametric models. If nothing happens, download Xcode and try again. STATS 607(B) STATS 608(B) STATS 600 STATS 607(A) STATS 507 STATS 610 STATS 620. admitted prior to Fall 2020 . CTools, an online learning management and collaboration system developed at U-M, was retired from service on August 28, 2020. endstream endobj startxref Course topics include: basic Monte Carlo methods (random number generators, variance reduction techniques, importance sampling and its generalizations), an introduction to Markov chains and Markov Chain Monte Carlo (Metropolis-Hastings and Gibbs samplers, data-augmentation techniques, convergence diagnostics). This is an advanced introduction to regression modeling and prediction, including traditional and modern computationally-intensive methods. Pre-requisites: STATS 520 or equivalent course in measure theory, STATS 620. Limit theorems, law of the iterated logarithm. Pre-requisite: STATS Master's Standing or stats 500. An individual instructor must agree to direct such a reading, and the requirements are specified when approval is granted. A solid background in linear algebra; knowledge of regression at the This course provides students with hands-on experience using a variety of techniques from modern applied statistics through case studies involving data drawn from various fields. A ccording to new research of more than 600 US businesses with 50-500 employees, 63.3% of companies say retaining employees is actually harder than hiring them. (3 Credits). Requirements Met? Statistics 580: Methods and Theory of Sample Design (SOC 717/BIOS 617), Theory underlying sample designs and estimation procedures commonly used in survey practice. Nonresponse weighting adjustments and imputation. Pre-requisites: MATH 597. If nothing happens, download GitHub Desktop and try again. (3 Credits), Statistics 605: Advanced Topics in Modeling and Data Analysis, This course covers recent developments in statistical modeling and data analysis. (3 Credits), This course covers the important reliability concepts and methodology that arise in modeling, assessing, and improving product reliability and in analyzing field and warranty data. Nonresponse weighting adjustments and imputation. Statistics 808: Seminar in Applied Statistics I, Statistics 809: Seminar in Applied Statistics II. Pre-requisite: Graduate level courses in Statistics at the level of STATS 500 and 501 or permission of instructor. This course continues Stats 611, covering nonparametrics (nonparametric regression, splines, kernel methods, density estimation, risk, generalization bounds, overfitting); resampling and data splitting methods (cross-validation, stability selection, data splitting, parametric and nonparametric bootstrap), statistical problems in high dimensions (white noise model, classical nonparametrics, Stein’s paradox, the Lasso and related algorithms and penalties. The course is a self-contained rigorous measure-theoretic introduction to probability theory. 3 credits . The second half of the course will survey tools for handling structured data (regular expressions, HTML/JSON, databases), data visualization, numerical and symbolic computing, interacting with the UNIX/Linux command line, and large-scale distributed computing. Statistics 631: Advanced Time Series Analysis. A substantial part of the course is devoted to computational algorithms based on Markov Chain Monte Carlo sampling for complex models, sequential Monte Carlo methods, and deterministic methods such as variational approximation. Statistics 711: Special Topics in Theoretical Statistics II. Comments: Signature Date Additional Graduate Courses. (4 credits). Graduate standing. (3 Credits). (3 Credits). It starts with a review of topics in probability theory including densities, expectation, random vectors and covariance matrices, independence, and conditioning. Stochastic processes; Wiener-Levy, infinitely divisible, stable. Topics covered will include modeling and estimation of data from heavy-tailed distributions, models and inference with multivariate copulas, linear and non-linear time series analysis, and statistical portfolio modeling. (3-4 Credits). be an opportunity to discuss homework sets. (4 Credits), Statistics 504: Practice and Communication in Applied Statistics. Problem sets will be linear models for independent observations using least squares Any approved STATS 600-level or above courses Cognate Courses Students may take up to 6 credits (equivalently, two courses) from departments other than Statistics or Biostatistics to fulfill the elective coursework requirement, with prior approval from their advisor Statistics 630: Topics in Applied Probability, Advanced topics in applied probability, such as queueing theory, inventory problems, branching processes, stochastic difference and differential equations, etc. (1 Credit), Statistics 550: Bayesian Decision Analysis (IOE 560), Axiomatic foundations for personal probability and utility; interpretation and assessment of personal probability and utility; formulation of Bayesian decision problems; risk functions, admissibility; likelihood principle and properties of likelihood functions; natural conjugate prior distributions; improper and finitely additive prior distributions; examples of posterior distributions, including the general regression model and contingency tables; Bayesian credible intervals and hypothesis tests; applications to a variety of decision-making situations. A special topic is chosen for a particular semester, with relevant methods drawn from a wide variety of disciplines, including economics, education, epidemiology, psychology, sociology, and statistics.