🐩 Stoffer’s GitHome
David S. Stoffer is Professor Emeritus
at (or near) the University of Pittsburgh
✨ The R package astsa along with the latest news
✨ General info and the astsa
changelog is at the NEWS page
✨ FUN WITH ASTSA has many demonstrations of astsa
capabilities
✨ All the R code used in Time Series Analysis and Its Applications (edition 5)
✨ All the R code used in Time Series: A Data Analysis Approach Using R
✨ Time Series Course @ Data Camp
✨ The Time Series R Issues page is back with more issues than ever
✨ The Time Series Graphics page is also alive again
✨ An R tutorial (was Appendix R in the time series texts and removed in edition 5)
✨ How to EASILY Link Math Libraries (MKL) to R on Windows
✨ Nicky Poison’s road map page
✨ So… you think you know Seinfeld
📧
📚 Reading Material
 Website for Time Series Analysis and Its Applications: With R Examples (4th Edition) by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2017.
You unlock this door with the key of imagination. Beyond it is another dimension… a dimension of sound, a dimension of sight, a dimension of mind. You’re moving into a land of both shadow and substance, of things and ideas. You’ve just crossed over into… T i M e S e R i e S a N a L y S i S
 Website for Time Series: A Data Analysis Approach Using R by R.H. Shumway and D.S. Stoffer. Chapman & Hall, 2019.
There is a fifth dimension beyond that which is known to man. It is a dimension as vast as space and as timeless as infinity. It is the middle ground between light and shadow, between science and superstition, and it lies between the pit of man’s fears and the summit of his knowledge. This is the dimension of imagination. It is an area which we call T i M e S e R i e S.
 Website for Douc, Moulines & Stoffer (2014) Nonlinear Time Series: Theory, Methods and Applications with R Examples Chapman & Hall Texts in Statistical Science.
You’re travelling through another dimension, a dimension not only of sight and sound but of mind; a journey into a wondrous land whose boundaries are that of imagination. That’s the signpost up ahead  your next stop … N o N L i N e a R T i M e S e R i e S

Stoffer, D. S. (2023). AutoSpec: Detection of narrowband frequency changes in time series. Statistics and Its Interface, 16(1), 97108. Article here  the code is in astsa as autoSpec.

Gong, C. and Stoffer, D.S. (2021), A Note on Efficient Fitting of Stochastic Volatility Models. J. Time Ser. Anal., 42: 186200. Article here  the code is in astsa as SV.mcmc.

AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series (with Ori Rosen and Sally Wood). Journal of the American Statistical Association, 15751589, 2012. AdaptSPEC.pdf
There is an R package for this called BayesSpec
Matlab programs are also available from Ori: mAdaptSPEC.zip

Local Spectral Analysis via a Bayesian Mixture of Smoothing Splines (with Ori Rosen and Sally Wood). Journal of the American Statistical Association, 249262, 2009. mixss.pdf

Smoothing Spline ANOPOW (with Sangdae Han, Li Qin and Wensheng Guo). Journal of Statistical Planning and Inference (special volume in honor of Manny Parzen  thanks Manny for all the slaps, with extra force, on the back), 37893796, 2010. ssanopow.pdf

A Stochastic Volatility Mixture Model: Estimation in the Presence of Irregular Sampling via Particle Methods and the EM Algorithm (with J. Kim). Journal of Time Series Analysis, 29, Issue 5, 811833, 2008. svmm.pdf
Matlab programs are also available: jkmatlab.m

Automatic Estimation of Multivariate Spectra via Smoothing Splines (with O. Rosen). Biometrika, 94, 335345, 2007. multspec.pdf.

A ResidualsBased Transition Model for Longitudinal Analysis with Estimation in the Presence of Missing Data (with T. KoruSengul). Statistics in Medicine 26, 33303341, 2007. tmla.pdf
The code for SAS, Splus and R.

Local spectral analysis via a Bayesian mixture of smoothing splines (with O. Rosen and S. Wood). Journal of the American Statistical Association, 104(485), 249262. mix.pdf

Discrimination and Classification of Nonstationary Time Series using the SLEX Model (with HY Huang & H. Ombao). Journal of the American Statistical Association, 99, 763774, 2004. hos04.pdf.
The Matlab programs

Resampling in State Space Models. Chapter 9 (pp. 171202) of State Space and Unobserved Component Models: Theory and Applications. Cambridge University Press, 2004. booty.pdf

Local Spectral Envelope: An Approach Using Dyadic Tree Based Adaptive Segmentation (with H. Ombao and D.E. Tyler). Annals of the Institute of Statistical Mathematics, 54, 201223, 2002. lospen.pdf

The Spectral Envelope and Its Applications (with D.E. Tyler & D.A. Wendt). Statistical Science. 15(3): 224253 (2000). specrev.pdf

Stoffer, D.S. (1999). Detecting common signals in multiple time series using the spectral envelope. Journal of the American Statistical Association, 94, 13411356. sigs.pdf

Stoffer, D.S. & Tyler, D.E. (1998). Matching sequences: Cross spectral analysis of categorical time series. Biometrika, 85, 201213. match.pdf

McDougall, A.J., Stoffer, D.S. & Tyler, D.E. (1997). Optimal transformations and the spectral envelope for realvalued time series. Journal of Statistical Planning and Inference, 57, 195214. mst97.pdf

Stoffer, D.S., Tyler, D.E. & McDougall, A.J. (1993). Spectral analysis for categorical time series: Scaling and the spectral envelope. Biometrika, 80, 611622. spenv.pdf

Stoffer, D.S. (1991). WalshFourier analysis and its statistical applications (with discussion). Journal of the American Statistical Association, 86, 462483. walshapps.pdf
Fortran program to calculate the finite Walsh transform: wft.for
 Stoffer, D.S., Scher, M., Richardson, G., Day, N. & Coble, P. (1988). A WalshFourier analysis of the effects of moderate maternal alcohol consumption on neonatal sleepstate cycling. Journal of the American Statistical Association, 83, 954963. stoffer88.pdf
The data are included in astsa as columns in
sleep1
andsleep2
. This paper won the American Statistical Association’s Outstanding Statistical Application Award for 1989. The theory for this paper was given in Stoffer (1987)… just below:

Stoffer, D.S. (1987). WalshFourier analysis of discretevalued time series. Journal of Time Series Analysis, 8, 449467. discrete.pdf

Stoffer, D.S. (1990). Multivariate WalshFourier Analysis. Journal of Time Series Analysis, 11, 5773. mwalsh.pdf
The data are included in astsa as
sleep1
andsleep2
.

Shumway, R.H. & Stoffer, D.S. (1992). Dynamic linear models with switching. Journal of the American Statistical Association, 86, 763769. dlmws.pdf

Shumway, R.H. & Stoffer, D.S. (1982). An approach to time series smoothing and forecasting using the EM algorithm. Journal of Time Series Analysis, 3, 253264. em.pdf
R code for the algorithm is in astsa and more details are in Chapter 6 of Time Series Analysis and Its Applications: With R Examples
 Stoffer, D.S. (1986). Estimation and identification of spacetime ARMAX models in the presence of missing data. Journal of the American Statistical Association, 81, 762772. starmax.pdf
The data (details in the 1st file): cpue1, cpue2, cpue3, cpue4, cpue5

Carlin, B.P., Polson, N.G. & Stoffer, D.S. (1992). A Monte Carlo approach to nonnormal and nonlinear state space modeling. Journal of the American Statistical Association, 87, 493500. gibbs.pdf

Stoffer, D.S. & Wall, K. (1991). Bootstrapping state space models: Gaussian maximum likelihood estimation and the Kalman filter. Journal of the American Statistical Association, 86, 10241033. boots.pdf
The material in this paper is discussed in Chapter 6 of Time Series Analysis and Its Applications: With R Examples. There is a demonstration at FUN WITH ASTSA, which is more fun than a barrel of monkeys.
 A list of papers at PubMed