🐩 Stoffer’s GitHome

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), 97-108. 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: 186-200. 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, 1575-1589, 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, 249-262, 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), 3789-3796, 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, 811-833, 2008. svmm.pdf

Matlab programs are also available: jkmatlab.m

Automatic Estimation of Multivariate Spectra via Smoothing Splines (with O. Rosen). Biometrika, 94, 335-345, 2007. multspec.pdf.
A Residuals-Based Transition Model for Longitudinal Analysis with Estimation in the Presence of Missing Data (with T. Koru-Sengul). Statistics in Medicine 26, 3330-3341, 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), 249-262. mix.pdf
Discrimination and Classification of Nonstationary Time Series using the SLEX Model (with H-Y Huang & H. Ombao). Journal of the American Statistical Association, 99, 763-774, 2004. hos04.pdf.

The Matlab programs

Resampling in State Space Models. Chapter 9 (pp. 171-202) 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, 201-223, 2002. lospen.pdf
The Spectral Envelope and Its Applications (with D.E. Tyler & D.A. Wendt). Statistical Science. 15(3): 224-253 (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, 1341-1356. sigs.pdf
Stoffer, D.S. & Tyler, D.E. (1998). Matching sequences: Cross spectral analysis of categorical time series. Biometrika, 85, 201-213. match.pdf
McDougall, A.J., Stoffer, D.S. & Tyler, D.E. (1997). Optimal transformations and the spectral envelope for real-valued time series. Journal of Statistical Planning and Inference, 57, 195-214. 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, 611-622. spenv.pdf
Stoffer, D.S. (1991). Walsh-Fourier analysis and its statistical applications (with discussion). Journal of the American Statistical Association, 86, 462-483. 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 Walsh-Fourier analysis of the effects of moderate maternal alcohol consumption on neonatal sleep-state cycling. Journal of the American Statistical Association, 83, 954-963. stoffer88.pdf

The data are included in astsa as columns in sleep1 and sleep2. 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). Walsh-Fourier analysis of discrete-valued time series. Journal of Time Series Analysis, 8, 449-467. discrete.pdf
Stoffer, D.S. (1990). Multivariate Walsh-Fourier Analysis. Journal of Time Series Analysis, 11, 57-73. mwalsh.pdf

The data are included in astsa as sleep1 and sleep2.

Shumway, R.H. & Stoffer, D.S. (1992). Dynamic linear models with switching. Journal of the American Statistical Association, 86, 763-769. 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, 253-264. 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 space-time ARMAX models in the presence of missing data. Journal of the American Statistical Association, 81, 762-772. 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, 493-500. 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, 1024-1033. 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

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