STSCI 6740

STSCI 6740

Course information provided by the Courses of Study 2020-2021. Courses of Study 2021-2022 is scheduled to publish by July 1.

Focuses on the modern theory of statistical inference, with an emphasis on nonparametric and asymptotic methods. Topics include empirical Bayes and shrinkage estimators,  unbiased estimation of risk, adaptive estimation, as well as oracle inequalities, a powerful concept which has  applications in classification and machine learning. An optional topic is the use of Markov random fields for image restoration. The course includes a discussion of the general asymptotic theory for statistical models, as a tool for finding optimal decisions, based on the concepts of contiguity and local asymptotic normality.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: MATH 6710 (measure theoretic probability) and STSCI 6730/MATH 6730, or permission of instructor.

View Enrollment Information

Enrollment Information
Syllabi: none
  •   Regular Academic Session.  Combined with: MATH 6740

  • 4 Credits Stdnt Opt

  • 17073STSCI 6740  LEC 001

    • TRTo Be Assigned
    • Aug 26 - Dec 7, 2021
    • Nussbaum, M

  • Instruction Mode: Planned for In Person