Homework assignments, project instructions, lecture notes and handouts will be posted in Canvas. Readings will be denoted by “550” for the Course Notes: Statistics 550 Advanced Mathematical Statistics and by “505” for A Pair of Primers: Primer on Matrix Analysis and Primer on Linear Statistical Models. Topics on calendar are subject to change.

Week 1: Jan 13, 15

Reading:

  • Course syllabus and Canvas exploration
  • 505: Chapters 2 and 7, Sections 8.1-8.4 and 8.11
  • 550: Chapter 2

Tuesday

Thursday

  • Kronecker products and the vec operator

Week 2: Jan 20, 22

Tuesday

  • Kronecker products and the vec operator (cont)

Thursday

  • Application of matrix operations in linear models
  • Homework 1 Due in class

Week 3: Jan 27, 29

Reading:

  • 505: Chapter 12
  • 550: Chapter 3

Tuesday

  • Matrix differentiation

Thursday

  • Matrix differentiation (cont)
  • Quiz 1: Matrix operations, Kronecker products, Vec

Week 4: Feb 3, 5

Reading:

  • 505: Chapter 9, Chapter 11 (skim), Section 15.1
  • 550: Chapter 5

Tuesday

  • Matrix differentiation (cont)
  • Minimizing SSE from a projection operator perspective

Thursday

  • Eigenvalue/eigenvector review
  • Homework 2 Due in class

Week 5: Feb 10, 12

Reading:

  • 505: Sections 13.1-13.4
  • 550: Chapters 4 and 7
  • Sections 5.5.1-5.5.3 in Casella and Berger

Tuesday

  • Eigenvalue/eigenvector review (cont)

Thursday

  • Order of magnitude
  • Quiz 2: Matrix differentiation and matrix representation of linear models

Week 6: Feb 17, 19

Tuesday

  • Order of magnitude (cont)

Thursday

  • Types of convergence in probability
  • Order of magnitude in probability

Week 7: Feb 24, 26

Tuesday

  • No class: Either attend the CLS Teaching Workshop with Dr. James Lang (registration required) or spend this time working on your project.
  • Project: Journal article proposal due in Canvas by 5pm

Thursday

  • Order of magnitude in probability (cont)
  • Multivariate Taylor series expansions
  • Homework 3 Due in class

Week 8: Mar 3, 5

Tuesday

  • Newton-Raphson and Fisher Scoring algorithms

Thursday

  • Newton-Raphson and Fisher Scoring algorithms for logistic regression

Week 9: Mar 10, 12

Tuesday

  • Newton-Raphson and Fisher Scoring algorithms for logistic regression (cont)
  • Quiz 3: Eigenvalues and eigenvectors, Order of convergence

Thursday

  • Missing data and the EM algorithm
  • Homework 4 Due in class

Spring break: Mar 16-20

Week 10: Mar 24, 26

Tuesday

  • Missing data and the EM algorithm (cont)

Thursday

  • Missing data and the EM algorithm (cont)

Week 11: Mar 31, Apr 2

Tuesday

  • Project: Executive summary due in Canvas by 5pm

Thursday

  • TBD

Week 12: Apr 7, 9

Tuesday

  • TBD

Thursday

  • TBD

Week 13: Apr 14, 16

Tuesday

  • Project: Proof sketch due in Canvas by 5pm

Thursday

  • TBD

Week 14: Apr 21, 23

Tuesday

  • TBD

Thursday

  • TBD

Week 15: Apr 28, 30

Project: Presentation slides due in Canvas by 5pm on Monday

Tuesday

  • Project presentations

Thursday

  • Project presentations

Finals week

  • Final exam in Wilson Hall 1-128 on Tuesday, May 5, 8:00am-9:50am