|Prof:||Robert L. Wolpertfirstname.lastname@example.org||OH: Wed 1:30-2:30pm, 211c Old Chem|
|TA:||TBD||TBD@duke.edu||OH: Wed 4:00-6:00pm, 211a Old Chem|
|Class:||Wed Fri 8:30-9:45am, 123 Old Chem|
|Text:||Larry Wasserman,||All of Statistics (errata, website)|
|Opt'l:||G Young & R Smith,||Essentials of Statistical Inference|
|G Casella & R Berger,||Statistical Inference (2/e)|
|A Gelman, J.B. Carlin, et al.||Bayesian Data Analysis (3/e)|
|I. Foundations & Estimation||Problems||Due|
|Jan -09||Models & Inference||hw1||Jan 16|
|Jan 14-16||Estimating CDFs and Statistical Functionals||hw2||Jan 23|
|Jan 21-23||Parametric Inference I: MoM & MLEs||hw3||Jan 30|
|Jan 28-30||Parametric Inference II: Properties & Asymptotics||hw4||Feb 06|
|Feb 04-06||Bayesian & Objective Bayes Estimation||hw5||Feb 11|
|Feb 11-13||Review & in-class Midterm Exam I (pdf sheet)||Hists:||Exam, Course|
|Feb 18-20||Confidence and Credible Interval Estimates||hw6||Mar 06|
|Feb 25-27||Winter storm|
|II. Testing Statistical Hypotheses|
|Mar 04-06||P-values, Significance, & Hypothesis Tests||hw7||Mar 20|
|--- Spring Recess (Mar 07-15) ---|
|Mar 18-20||Likelihood Ratios & Neyman-Pearson Tests||hw8||Mar 27|
|Mar 25-27||Bayes Factors & Bayesian Testing||hw9||Apr 01|
|Apr 01-03||Review & in-class Midterm Exam II||Hists:||Exam, Course|
|III. Models & Methods|
|Apr 08-10||Empirical and Hierarchical Bayes||hw10||Apr 15|
|Apr 15-||Review for Final Exam|
|Apr 29||7-10pm Wed: In-class Final Examination||Hists: exam, course|
Lecture notes are available on-line (click on the "Week" column if it's blue or green). If you bring a copy of these to lectures you can spend more time understanding and less time writing. These notes are a work-in-progress, and will evolve as I try to improve them by adding material, correcting errors, and clarifying difficult points. If something in the notes looks wrong or confusing, first check to see if the website has a more recent version (refresh your browser, and look at the "Last edited" date at the bottom of the last page). If it still looks puzzling, please e-mail me with a question or comment so I can fix it if it's wrong, try to explain better or add an example if it's confusing, or help you understand if it was just a difficult issue.
This is syllabus is also tentative, last revised , and will almost surely be superseded— reload your browser for the current version.
Statistical modeling and inference depend on the mathematical theory of probability, and solving practical problems usually requires integration or optimization in several dimensions, either analytically or numerically. Thus this course requires a solid mathematical background: multivariate calculus at the level of Duke's MTH212 or MTH222 and linear algebra at the level of Duke's MTH221 or MTH216. Students must be proficient in calculus-based probability theory at the level of MTH230/STA230.
Some questions will be computational, and will require skill in any one of the computing environments commonly used in statistical analysis such as R, Matlab, or Python. Students without strong preparation in these will need to invest significant additional time to fill in the gaps. Don't expect spreadsheets or calculators to be sufficient.
Weekly problem sets are assigned on the class website here. Homeworks are collected at the start of each Friday class (so I can answer questions about them in class) and are returned at the following Wednesday class. LaTeX'd homework assignments can also be submitted electronically as pdf attachments to an e-mail sent to email@example.com. Until solutions are posted, late homeworks are accepted but are penalized 10% per day. The lowest homework score will be dropped. Exam week homeworks are due on Wednesday, so you can ask questions about them and get feedback before the test.
Homework problems are awarded points based on your success in communicating a correct solution. For full credit the solution must be clear, concise, and correct; even a correct solution will lose points or be returned ungraded if it is not clear and concise. Neatness counts. Consider using LaTeX and submitting your work in pdf form if necessary (it's good practice anyway).
You may discuss and collaborate in solving homework problems, but you may not copy— each student should write up his or her solution. Cheating on exams, copying or plagiarising homeworks or projects, lying about an illness or absence and other forms of academic dishonesty are a breach of trust with classmates and faculty, and will not be tolerated. They also violate Duke's Community Standard and will be referred to the Graduate School Judicial Board or the Dean of the Graduate School.