Prof: | Robert L. Wolpert |
wolpert@stat.duke.edu | OH: Mon 2:00-3:00pm, 211c Old Chem | ||||

TAs: | Jialiang Mao |
jialiang.mao@duke.edu | OH: Tue 7:00-9:00pm, 211a Old Chem | ||||

Xu Chen |
xu.chen2@duke.edu | OH: Wed 7:00-9:00pm, 211a Old Chem | |||||

Class: | Tue Thu 1:25-2:40pm, 116 Old Chem | ||||||

Opt'l: | G Young & R Smith, | Essentials of Statistical Inference | (On-line, Duke only) | ||||

G Casella & R Berger, | Statistical Inference (2/e) | ||||||

A Gelman, JB Carlin, et al. | Bayesian Data Analysis (3/e) |

f

Week | Topic | Homework | |
---|---|---|---|

I. Foundations & Estimation |
Problems | Due | |

Jan -14 | Models & Inference | hw1 | Jan 21 |

Jan 19-21 | Estimating CDFs and Statistical Functionals | hw2 | Feb 04 |

Jan 26-28 | No class (Volcano workshop at Kilauea) | ||

Feb 02-04 | Parametric Inference I: MoM & MLEs & Fish Info | hw3 | Feb 11 |

Feb 09-11 | Parametric Inference II: Properties & Asymptotics | hw4 | Feb 18 |

Feb 16-18 | Subjective & Objective Bayesian Estimation | hw5 | Feb 25 |

Feb 23-25 | Confidence and Credible Interval Estimates | hw6 | Mar 01 |

Mar 01-03 | Review & in-class Midterm Exam I (S15) | Hists: | Exam, Course |

II. Testing Statistical Hypotheses | |||

Mar 08-10 | P-values, Significance, & Hypothesis Tests
| hw7 | Mar 24 |

--- Spring Recess (Mar 12-20) --- | |||

Mar 22-24 | Likelihood Ratios & Neyman-Pearson Tests | hw8 | Mar 31 |

Mar 29-31 | Bayes Factors & Bayesian Testing | hw9 | Apr 12 |

Apr 05-07 | No class (SIAM/ASA Conference in Lausanne) | ||

Apr 12-14 | Review & in-class Midterm Exam II (S15) | Hists: | Exam, Course |

Apr 19 | Empirical and Hierarchical Bayes | hw10 | Apr 26 |

Apr 26 | Review for Final Exam | ||

May 06 | 7-10pm Fri: In-class Final Examination (S15) | Hists: exam, course |

There is no textbook for the course but lecture notes are available on-line (click on the "Week" column if it's blue or green). If you bring a copy of these notes 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 send me an e-mail 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. I'm not aware of any book that covers both theoretical and computational aspects of both Bayesian and Frequentist (or sampling-based) statistics at the level we need. The book cited above by Young and Smith comes as close as any and costs under $35, so I'd recommend it as a companion if you'd like a second perspective on some course topics.

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, and in particular should be familiar with the most
common probability distributions (here is a
list of most of them, in the notation we'll be using in this course,
and here is a brief discussion).

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 Thursday class (so I can answer
questions about them in class) and are returned at the following Tuesday
class. LaTeX'd homework assignments can also be submitted electronically
as pdf attachments to an e-mail sent to
*sta532@stat.duke.edu*.
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 *Tuesday* to give you a chance to 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).

In-class Midterm and Final examinations are closed-book and closed-notes with one 8½"×11" sheet of your own notes permitted. Tests from a recent STA532 offering are available to help you know what to expect and to help you prepare for this year's tests:

Spring 2015: | 1st Midterm | 2nd Midterm | Final Exam |
---|

- There are enough seats in the room, and
- They are willing to commit to active participation.

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 plagiarizing 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.