Advanced Statistical Computing II

L87 SDS 5532

This is the second course on advanced methods and tools for Statistical Computing. This course will introduce classical methods, including the EM algorithm and its variants. It also will cover basic convex optimization theory and advanced computing tools and techniques for big data and learning algorithms. Prereq: Math 233; a course in linear algebra at level of Math 309 or Math 429; multivariable-calculus-based probability and mathematical statistics (Math/SDS 493-494 or Math/SDS 3211/4211); Experience with a high-level programming language like R, Python, C++, etc.

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2:30P-3:50P

Duncker / 1

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Advanced Topics in Statistics: Time Series and High-dimensional Data Analysis

L87 SDS 5801

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This is an advanced topics course on time series analysis and high-dimensional statistics. It will provide a systematic introduction to two research topics: selfnormalization (SN) for time series inference and nonlinear dependence metrics and their statistical applications. For self-normalization, we plan to cover its use for both confidence interval construction and hypothesis testing in the setting of stationary multivariate time series, functional time series, and high-dimensional time series. Change-point testing and estimation based on self-normalization will be introduced in detail for both low and high-dimensional data. Some recent work which combines sample splitting and self-normalization will also be presented. The course assumes that the student has the basic background of time series analysis and some research experience in time series analysis is desired but not a prerequisite. For nonlinear dependence metrics, the emphasis will be placed on distance covariance, energy distance and their variants, including Hilbert-Schmidt Independence Criterion, maximum mean discrepancy, and martingale di?erence divergence, among others. The usefulness of these metrics will be demonstrated in some contemporary problems in statistics, such as dependence testing and variable screening/selection for high-dimensional data, as well as dimension reduction and diagnostic checking for multivariate time series. Some recent work on their applications to the inference of non-Euclidean data will also be discussed. The presentations are based on the research results my collaborators and I have obtained in the past and will cover methodology, theory and practical data examples.

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4:00P-5:20P

Simon / 017

Shao

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Advanced Topics in Statistics: Five Statistical Insights You Need to Know

L87 SDS 5802

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This advanced topic course delves into five critical ideas and insights that have significantly impacted the fields of statistics and data science. From foundational concepts to influential advancements, each topic is selected for its historical significance and enduring impact. Students will develop a deep appreciation for these key ideas, associated insights, and their role in shaping contemporary statistical theory and practice. For each topic, we will discuss motivations, innovations, and impacts through presentations and discussions. Students will be required to read important papers and share their perspectives. Although many topics fit this criterion, the list includes, but is not limited to, influence functions, resampling, adaptive designs, dimension reduction, data augmentation, regularization, identifiability, and propensity scores. Depending partly on the interests of the students, we will select and focus on five topics over the course of one semester. Prerequisites: SDS 5020 and 5071.

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4:00P-5:20P

Eads / 203

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See the beginning of the mathematics listings and register for the section corresponding to supervising instructor. Prerequisite: Graduate standing and permission of the instructor.

L87 SDS 590

Research

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Dissertation/Research

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Ding

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Figueroa-Lopez

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Lahiri

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Kuffner

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Lin

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Lunde

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Mondal

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Chen

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Madrid Padilla

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Chen

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Guinness

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Practical Training in Statistics

L87 SDS 591

The Master of Arts in Statistics program at Department of Statistics and Data Science, Washington University in St. Louis, requires students to participate in extensive practical training as an essential component of the degree program. The program requires all full-time students to participate in practical training at least for one semester or summer session during their degree study. This requirement should be completed prior to the last semester in the degree program. The requirement does not require registration for additional credit but does require registration by ALL students, regardless of citizenship or visa status, for the zero-credit practical training course MATH 591 for one semester or summer session in which a student participates in an internship or co-op. Practical training can be fulfilled by any one of the following three methods: 1. An off-campus Internship or Co-op position with an employer in the data science industry or data science related department of a company is STRONGLY RECOMMENDED as the most preferred component of the Practical Training. The position should be related to the Statistics curriculum and span at least four weeks in duration. The student is required to submit a written report after the internship ends. 2. On-campus research, or research project participation, where the research or project is related to data science under the sponsorship of one or more of a data science institution, industry practitioner or faculty member of Washington University in St. Louis. A detailed written report on the research or project participation should be submitted and approved by a faculty member in the Department of Mathematics and Statistics. 3. Participation in the colloquium or statistics seminar in Department of Mathematics and Statistics, or other data science related research colloquium and seminar talks at Washington University in St. Louis. Students must attend talks regularly. A written report should be submitted to summarize the problems, ideas, approaches and results learned from at least four talks, and provide additional information from further reading and research of the topic.

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11:00A-11:50A

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Lin

May 6 2025 10:30AM - 12:30PM

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