TIME SERIES AND MULTIVARIATE ANALYSIS

Instructor: Richard L. Smith

This page was last updated April 18, 2019.

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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