STAT 5093, Computational Statistics Location: MSCS 317 Time: MWF 9:30 - 10:20 Primary Textbook: Elements of Computational Statistics (ECS) by James E

Syllabus Computational Statistics

Course: STAT 5093, Computational Statistics Location: MSCS 317 Time: MWF 9:30 - 10:20 Primary Textbook: Elements of Computational Statistics (ECS) by James E. Gentle. Other Textbook: Computer Age Statistical Inference (CASI) by Efron and Hastie. Instructor: Joshua D. Habiger Oﬃce: 301 MSCS Email: [email protected] Phone: 405-744-9657 Oﬃce Hours: TU 10:15 - 11:00 or by appointment.

Prerequisites: STAT 5013 (Statistics for Experimenters I, STAT 5223 (Sta- tistical Inference).

Book: I’ll follow Elements of Computational Statistics (ECS) by James E. Gen- tle somewhat closely, especially in earlier more fundamental topics. Other more recent topics will be taken from Computer Aged Statistical Inference (CASI). This book is at https://web.stanford.edu/~hastie/CASI/. Both books cover essentially the same material, with the latter covering a few new topics and pro- viding more examples.

Overview: We will cover optimization algorithms in basic statistical inference, (Markov chain) Monte Carlo methods, permutation testing, cross validation and jacknife methods, bootstrapping, E-M algorithm and missing data analysis, statistical machine learning topics, and false discovery rates.

Software: I require that you use R. Some homework questions and your project will require that you submit (documented) R code and output.

Homework: • Material: There will be a homework assignment about every 3 or 4 class periods. They will be posted more than a week before they are due and should be started as soon as they are posted. Homework assignments are worth a total of 30% of your grade. Late homework will be accepted for 50% credit the following class period and you must alert me that you will be turning in late homework on or before the original due date. • Grading / rules: You are allowed and encouraged to work together and use any other resource available on homework. But solutions and code must be written independently. If you are not sure whether an event constitutes cheating ask me ﬁrst.

Exams: There will be two midterm exams and one ﬁnal exam worth 15, 15,

1 Syllabus Computational Statistics and 30% in class. The midterm exams are tentatively scheduled for the 1st week of October and the 2nd week of November. The ﬁnal exam will be Thursday, Dec. 12, 9:00 - 10:50 am. The exact midterm exam dates will be announced at least 1 week prior.

Project: You will have a ﬁnal written and oral project worth 10% of your grade. The most likely project will involve the comparison of several statistical methods with a simulation. Note that the statistical methods, metrics for their assessment, and the simulation study design and results must be rigorously de- scribed. Further, any second year graduate student in statistics must be able to read your project description and understand it without reading dozens of other papers to get caught up. The required level of rigor coupled with your target audience likely means that the simulation section of your dissertation is way too complex to present. A good place to look for simpler methods to compare is in The American Statistician. We’ll discuss projects in more detail after the ﬁrst exam.

Grading Scale: I will use the 90-80-70-60 grading scale.