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STATS 607(B) STATS 608(B) STATS 600 STATS 607(A) STATS 507 STATS 610 STATS 620. 3 credits . (3 Credits), Decomposition of series; trends and regression as a special case of time series; cyclic components; smoothing techniques; the variate difference method; representations including spectrogram, periodogram, etc. It will cover topics from orthogonal arrays, optimal designs, minimum aberration designs, parameter design, response surface methodology, computer experiments, and experiments with split-plot structure. Theoretical Statistics (at the level of Stats 426 or equivalent), Generalized linear models including logistics regression, Poisson regression, contingency tables. 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. Random effects and repeated measures. It includes a comprehensive treatment of linear models for independent observations using least squares estimation; non least-squares approaches including penalization methods for variable selection;  regression methods for dependent data, including generalized least squares, estimating equations, and mixed models; generalized linear models and generalized estimating equations; quantile regression, dimension reduction regression, and smoothing-based methods. Statistics 560: Introduction to Nonparametric Statistics (BIOS 685), Confidence intervals and tests for quantiles, tolerance regions, and coverages; estimation by U statistics and linear combination or order statistics; large sample theory for U statistics and order statistics; the sample distribution and its uses including goodness-of-fit tests; rank and permutation tests for several hypotheses including a discussion of locally most powerful rank and permutation tests; and large sample and asymptotic efficiency for selected tests. It starts with a review of topics in probability theory including densities, expectation, random vectors and covariance matrices, independence, and conditioning. (3 Credits), Pre-requisite: Math 215 (Calculus III) or equivalent, This is a graduate-level introductory course to key concepts, methods and theory in statistical inference. Graduate standing. Prerequisites: linear algebra; regression at the level of STATS 413; probability and statistical theory at the level of STATS 425/426. Additional topics may vary with the instructor. Author: McDonald, Judith Created Date: 1/20/2017 … Statistics 725: Topics in Advanced Probability I (MATH 725), Pre-requisite: STATS 626. This course is an introduction to measure-theoretic probability theory, with emphasis on rigorous treatment of the various topics discussed in the course. Experience with data analysis techniques at the level of STATS 500 and STATS 503 will be helpful. Statistics 605: Advanced Topics in Modeling and Data Analysis. Topics will be drawn from current research projects, will vary each semester. This course covers recent developments in statistical theory. basic programming skills. Statistics 553: Conceptual Foundations of Statistical Inference (PHIL 553), This course will focus on conceptual issues in the foundations of probability theory and statistics. 277 West Hall Visiting researchers will provide a brief account of their aims and data before defining the methodological challenge for which they desire discussion. The course reviews basic notions from matrix algebra and real analysis. (3 Credits), Decomposition of series; trends and regression as a special case of time series; cyclic components; smoothing techniques; the variate difference method; representations including spectrogram, periodogram, etc. Emphasis will be placed on new concepts/tools and recent advances. Analysis of expression array data. PSYCH 613, ECON 405) and graduate or advanced undergraduate standing, or permission of instructor. linear models for independent observations using least squares Statistics 626: Probability and Random Processes II (MATH 626), Selected topics from among: diffusion theory and partial differential equations; spectral analysis; stationary processes, and ergodic theory; information theory; martingales and gambling systems; theory of partial sums. 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. estimation, with some discussion of non least-squares approaches; (2) The quantitative data will be modeled in various ways including mixed models, and as functional data. The final course grade will be weighted 25% from the regular problem Additional topics in modern probability theory chosen by the instructor are covered in the last few weeks of the course. (3 Credits), Statistics 503: Statistical Learning II: Multivariate Analysis, The course covers methods for modern multivariate data analysis and statistical The seminar will consider statistical questions that arise in the physical sciences. The University of Michigan has, in various departments and in the Institute for Social Research, the faculty talent to be able to offer one of the best specializations in the country. (3 Credits). Permission of instructor required to register. Weekly hands-on problems will be presented on the algorithms presented in the course, the use of public sequence databases, the design of hidden Markov models. Topics covered include Markov chains in discrete and continuous time, Poisson processes, Brownian motion, random walks, and their applications in key scientific and engineering areas. Problem sets will be (3 Credits). (3 Credits), Statistics 503: Statistical Learning II: Multivariate Analysis, The course covers methods for modern multivariate data analysis and statistical (3 Credits). 90 0 obj <> endobj Statistics 547: Probabilistic Modeling in Bioinformatics (Math 547), Probabilistic models of proteins and nucleic acids. Pre-requisite: STATS 500 or background in regression. The following topics will be covered: (1) a comprehensive treatment of 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. Statistics 631: Advanced Time Series Analysis. Pre-requisite: Knowledge of linear algebra; Knowledge of regression and analysis of variance at the level of STATS 500; Knowledge of probability and statistical theory at the level of BIOSTAT 601/602. (3 Credits). GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. 0 Analysis of DNA/RNA and protein sequence data. Lectures  provide background on case studies, along with reviews of relevant methodology. 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. Contribute to kshedden/UMStats600 development by creating an account on GitHub. The theory of hypothesis testing is also covered, including uniformly most powerful tests and the duality between testing and interval estimation. Pre-requisite: Three or more courses in Statistics and preferably a course in methods of survey sampling. Students are strongly encouraged to take Stats 604 in their second year (Stats 600 is a prerequisite). The topics covered will include univariate and multivariate families of distributions, likelihood principle, point estimation, confidence regions, hypothesis tests, large sample properties, and other selected topics in contemporary methods. Algorithms for sequence alignment, statistical analysis of similarity scores, hidden Markov models, neural networks training, gene finding, protein family profiles, multiple sequence alignment, sequence comparison and structure prediction. (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. Pre-requisites: MATH 417 and either STATS 611 or BIOSTAT 602. Evaluation is based on attaining insight from the data, effective communication of findings, and appropriate use of statistical methodology, as well as active participation in class discussions. (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. A seminar will allow students and instructor to learn to formulate alternative modeling approaches. The course covers algorithms for large-scale matrix computations, majorization-minimization methods, Newton-type methods, and stochastic approximation. This course is restricted to Master in Applied Statistics and Masters in Data Science students only. 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. Modern regression techniques. The emphasis is not on specific methods, but rather on scientific reasoning, collaboration, communication, and critical evaluation of findings. This course covers core topics in statistical theory. Statistics 808: Seminar in Applied Statistics I, Statistics 809: Seminar in Applied Statistics II. Pre-requisite: STATS Master's Standing or stats 500. be an opportunity to discuss homework sets. Statistics 670: Advanced Design and Analysis of Experiments, This is an advanced course on the design and analysis of experiments.

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