Math 410 meets Monday and Wednesday 2.30pm - 4.25pm.
Homework is due on the first lecture the week after the week the homework is given: the first homework questions are all due on February 5.
|1||Jan 29, 31||Welcome, intro, structure, background, crash course in probability and measures||1.1 - 1.11 (skim)||1.11: 5, 10, 17, 20, 27, 45|
|2||Feb 5, 7||Models and estimators, sufficiency||3.1-4||3.7: 2, 4, 7, 11|
|3||Feb 14||Completeness||3.5||3.7: 16|
|4||Feb 20, 21||Rao-Blackwell; Unbiased estimators||3.6, 4.1-2||3.7: 31; 4.7: 1, 4, 5, 22|
|5||Feb 26, 28||Standard normals and Variance bounds||4.4-6 (skim proofs)||4.7: 26, 30,|
|6||Mar 5, 7||Hypothesis testing||12.1-3||12.8: 3, 4, 8, 17, 22|
|7||Mar 12, 14||Confidence intervals||9.4, 12.4||9.10:10, 12, 14; 12.8: 28|
|8||Mar 19, 21||Two-sided and unbiased tests||12.6-7|
|9||Mar 26, 28||More estimators: Maximum Likelihood & Method of Moments||9.2-3, 6, 9.10:2||9.10:8, 9|
|10||Apr 9||Large sample testing||17.1-4||17.5:2, 4a|
|11||Apr 16, 18||GLM: Canonical form and estimation||14.1-2||14.9:1a-d, 4a-d|
|12||Apr 23, 25||Generalized linear models||14.3-5||14.9:4e-f, 18|
|13||Apr 30, May 2||Categorical data and goodness of fit.||14.6-8||14.9:4g-h, 14|
|14||May 7, 9||Nonparametric testing||9th deadline draft|
|15||May 14, 16||Additional topics||16th deadline report|
|Renisa Myrtezaj||Bayesian Estimation||May 14, 14.30|
|Joseph Murgolo||Polynomial Regression||May 14, 15.00|
|Robert Ferrando||Nonparametric Regression||May 14, 15.30|
|Fatme Chour||ARMA||May 16, 14.30|
|Michael Khanis||Optimal Stopping||May 16, 15.00|
|Steve Gad||Critical Path Analysis||May 16, 15.30|
The source of your grades in this course will be on a written report. You have a choice of topic:
- Evaluating the statistical methods of a research paper. You will pick a paper to review by the third meeting of the course.
- Describing a statistical method not mentioned in the course.
Your paper report should contain
- A description of the objective of the paper
- A comprehensive description of the statistical methods chosen by the authors
- An evaluation of whether the methods chosen were appropriate. If you find that the authors made mistakes in choosing their analysis methods, you should suggest better choices.
- A reproduction of the statistical analyses made by the authors: compute the estimators they computed, and compare with the values the authors reported.
Your methods report should
- Comprehensively describe the statistical analysis methods and the data collection methods.
- Critically evaluate the applicability requirements and limitations of the method.
- Perform an analysis on a dataset using the method.
- Be presented in class
Each of these tasks is graded on a scale of Excellent / Good / Acceptable:
- Excellent is awarded for Good plus an additional requirement of suggestions and evaluations of alternatives
- Good is awarded for comprehensive coverage without any formal flaws.
- Acceptable is awarded for up to 4 minor flaws:
- language errors to the point of ambiguous text
- arithmetic errors that do not essentially change the qualitative argument.
Failing any one of the 4 tasks will fail the course.
The final will contribute with a chance to raise your grade by two grade steps along the sequence D → C → C+ → B- → B → B+ → A- → A