This course is designed to give a graduate-level student (and senior undergrads) a thorough grounding in properties about optimization and integrating problems in statistics and machine learning, and a broad …
An introduction to linear statistical models in the context of data science. Topics include simple and multiple linear regression, generalized linear models, time series, analysis of covariance, tree-based classification, and …
Course develops fundamental methodology to regression and linear-models analysis in general. Topics include model fitting and inference, partial and sequential testing, variable selection, transformations, diagnostics for influential observations, multicollinearity, and …
This course develops fundamental methodology to the analysis of multivariate data. Topics include the multivariate normal distributions, multivariate regression, multivariate analysis of variance (MANOVA), principal components analysis, factor analysis, and …
This course develops fundamental concepts and methodology in the design and analysis of experiments. Topics include analysis of variance, multiple comparison tests, completely randomized designs, the general linear model approach …
This course introduces fundamental concepts in probability that underlie statistical thinking and methodology. Topics include the probability framework, canonical probability distributions, transformations, expectation, moments and momentgenerating functions, parametric families, elementary …
This course develops fundamental methodology to the analysis of longitudinal data. Topics include data structures, modeling the mean and covariance, estimation and inference with respect to the marginal models, linear …
This course develops fundamental methodology to the analysis of categorical data. Topics include contingency tables, generalized linear models, logistic regression, and logit and loglinear models. Conceptual discussion in lectures is …
Course provides an introduction to Bayesian methods with an emphasis on modeling and applications. Topics include the elicitation of prior distributions, deriving posterior and predictive distributions and their moments, Bayesian …
This course develops skills in using data analysis to contribute to research. Each student completes a data-analysis project using data from an interdisciplinary research effort. Topics will vary, and are …
This course develops skills in reading the statistical research literature and prepares the student for contributing to it. Each student completes a well written and properly formatted paper that would …
This course provides the opportunity to offer a new topic in the subject area of statistics.
In this course, students will read, present, and discuss research papers on topics that are closed related to faculty's research interests, so that students have understandings of research profiles in …
This course develops skills in reading the statistical research literature and prepares the student for contributing to it. Each student completes a well written and properly formatted paper that would …
This course introduces fundamental concepts in the classical theory of statistical inference. Topics include sufficiency and related statistical principles, elementary decision theory, point estimation, hypothesis testing, likelihood-ratio tests, interval estimation, …
This course introduces fundamental concepts in probability from a measure-theoretic perspective. Topics include sigma fields, general measures, integration and expectation, the Radon-Nikodym derivative, product measure and conditioning, convergence concepts, and …
This course covers advanced theory and methodology in statistical inference. It includes, but is not limited to, substantial, in-depth coverage of topics in asymptotic inference. Context and additional topics vary …
This course covers advanced theory and methodology in statistical inference. It includes, but is not limited to, substantial, in-depth coverage of topics in asymptotic inference. Context and additional topics vary …
This course covers advanced theory and methodology in probability. It includes, but is not limited to, substantial, in-depth coverage of topics in stochastic processes. Context and additional topics vary by …
This course develops skills related to the practice of statistical consulting. It covers conceptual topics and provides experience with data analysis projects found in or resembling those in statistical practice. …