Stat 550 Course Calendar
Homework assignments, lecture notes and handouts will be posted in D2L. 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.
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Date
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Day
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Topics
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Reading/Assignment
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|---|---|---|---|
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Jan 19
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Thur
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Casella and Berger review; Course introduction; Kronecker products and the vec operator |
Syllabus; 550 Ch. 2; 505 Secs. 7.1, 7.2.1-7.2.3, 8.1-8.4
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Jan 24
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Tue
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Review Kronecker product and vec operator proof exercises |
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Jan 26
|
Thur
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Matrix differentiation |
550 Chapter 3; 505 Chapter 13 |
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Jan 31
|
Tue
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Matrix differentiation (cont); eigenvalue/eigenvector review |
505 Ch. 9 |
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Feb 2
|
Thur
|
Types of proofs; order of magnitude |
505 Sec. 2.2-2.3; 550 Sec. 5.1-5.2 Homework 1 Due |
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Feb 7
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Tue
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Review of types of convergence in probability; order of magnitude in probability |
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Feb 9
|
Thur
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Order of magnitude in probability(cont) |
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Feb 14
|
Tue
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Multivariate Taylor series; Newton-Raphson and Fisher Scoring algorithms |
550 Sec. 4.1; 550 Sec. 7.1-7.4; Casella and Berger Sec. 5.5
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Feb 16
|
Thur
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Newton-Raphson and Fisher Scoring algorithms for logistic regression |
Homework 2 Due
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Feb 21
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Tue
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Finish coding Newton-Raphson algorithm for logistic regression; Missing data and the EM algorithm |
550 Sec. 7.6
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Feb 23
|
Thur
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Missing data and the EM algorithm |
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Feb 28
|
Tue
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EM algorithm (cont) |
Journal Project: Article Proposal Due |
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Mar 2
|
Thur
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Coding the EM algorithm
|
Homework 3 Due |
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Mar 7
|
Tue
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Sufficiency Quiz 2: Order of magnitude and order of magnitude in probability (550 Ch 5) |
550 Sec. 9.1
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Mar 9
|
Thur
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Sufficiency (cont)
|
Journal Project: Executive Summary Due
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Mar 13-17
|
|
Spring Break
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Mar 21
|
Tue
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Sufficiency (cont)
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Mar 23
|
Thur
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Exponential families and invariance Quiz 3: Taylor series expansions, Newton-Raphson and Fisher Scoring algorithms (550 Ch 4, Sec 7.3-7.4) |
550 Sec. 9.2-9.3 Journal Project: Proof Sketch Due |
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Mar 27-31
|
|
Journal article presentations via D2L No class meetings |
Fri: Homework 4 Due
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Apr 4
|
Tue
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Equivariance and maximal invariants |
550 Sec. 9.3
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Apr 6
|
Thur
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Conditionality principle and ancillary statistics Quiz 4: Missing data, EM algorithm, sufficiency (550 Sec 7.6, 9.1)
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550 Sec. 9.4
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Apr 11
|
Tue
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Completeness |
550 Sec. 9.6
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Apr 13
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Thur
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Likelihood-based inference |
550 Ch. 10 Optional reading: Ly et al. (2017) Homework 5 Due |
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Apr 18
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Tue
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Likelihood-based inference (cont) Quiz 5: Exponential families, invariance, equivariance, maximal invariants, conditionality principle, ancillary statistics, completeness |
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Apr 20
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Thur
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Likelihood-based inference (cont) |
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Apr 25
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Tue
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Likelihood-based inference (cont) |
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Apr 27
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Thur
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Information criteria
|
550 Sec. 10.4 Homework 6 Due |
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May 2
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Tue
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Information criteria (cont) The likelihood principle and p-values Quiz 6: Score function, information, and likelihood-based inference |
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May 4
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Thur
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Generalized estimating equations
|
550 Ch. 13
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Finals Week |
Tue
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Final Exam: Tuesday, May 2, 10:00-11:50am
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