STOR 754, SPRING 2019
Instructor: Richard L. Smith

This page was last updated April 18, 2019.

Introductory Course Handout

The course STOR 754 is one of the Statistics program's "B-level" courses. Students in the Statistics program will have already taken the first-year or "A-level" courses. Students from other programs are welcome to take the course provided they have had roughly the following background: a graduate-level course in regression and linear models equivalent to STOR 664; and some exposure to graduate-level statistical theory at the level of the STOR 654/655. If in doubt, please email the instructor.

Required text: Time Series: Theory and Methods (Springer Series in Statistics) by Peter J. Brockwell, Richard A. Davis

Recommended text: Multivariate Analysis (Probability and Mathematical Statistics) (Paperback) by K.V. Mardia, J.T. Kent and J.M. Bibby.

These will be supplemented by the instructor's own course notes (see links below).

Topics, Time series: Stationary processes; autocovariances, spectral density, linear process, ARMA. Estimation, time and spectral domain methods. Prediction.

Topics, Multivariate analysis: Basic theory of the multivariate normal and Wishart distributions. Principal components, canonical correlations, factor analysis, cluster analysis.

Other topics that may be covered as time permits: Multivariate time series, state space models, long-range dependence, dimension-reduction techniques in multivariate analysis.

Software: The Brockwell-Davis book includes an excellent self-contained (and very user-friendly) time series package called ITSM, and part of the time we will use that. However, R also includes extensive facilities for both time series and multivariate analysis, so we will use that as well. No previous knowledge of ITSM is needed; some previous exposure to R is desirable but not essential. If you don't know R already you can download it here.

The online notes linked below include numerous references to S-PLUS programs. As you may know, S-PLUS has now largely been supplanted by R, and we won't be using S-PLUS in this course. However, many programs from S-PLUS are easily modified (or may need no modifying at all) to run in R.

Instructor's Course Notes:

Time Series Notes
Multivariate Analysis Notes
Time series datasets
S-PLUS programs for Chapter 6 of time series notes
Multivariate datasets
R code for multivariate examples
Addenda to Multivariate Analysis Notes (includes new R code and some minor corrections)

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