MATHEMATICAL STATISTICS I

Prerequisites & course information

Requisites: Declared in Statistics MS, Statistics PhD, Biomedical Data Science PhD, Biomedical Data Science MS, or Statistics Doctoral Minor Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Define statistical estimation problems using rigorous mathematicallanguages Audience: Graduate 2. Derive basic statistical properties of an estimator such as bias andvariance Audience: Graduate 3. Analyze and compare different statistical estimators, and articulatethe strengths and weaknesses of each method Audience: Graduate 4. Apply theoretical tools such as concentration inequalities to newstatistical problems in the form of mathematical proofs Audience: Graduate 5. Articulate the distinctions between classical large-sample statisticaltheory and high-dimensional statistical theory, and critique recentstatistical research Audience: Graduate Learning Outcomes: 1. Define statistical estimation problems using rigorous mathematicallanguages Audience: Graduate 2. Derive basic statistical properties of an estimator such as bias andvariance Audience: Graduate 3. Analyze and compare different statistical estimators, and articulatethe strengths and weaknesses of each method Audience: Graduate 4. Apply theoretical tools such as concentration inequalities to newstatistical problems in the form of mathematical proofs Audience: Graduate 5. Articulate the distinctions between classical large-sample statisticaltheory and high-dimensional statistical theory, and critique recentstatistical research Audience: Graduate