Tele:  624-8496
Fax:  648-8814
Some Papers + Data + Very Soft Ware
- 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
- 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
AdaptSPEC: Adaptive Spectral Estimation
for Nonstationary Time Series (with Ori Rosen and Sally Wood).
Journal of the American Statistical Association, 1575-1589, 2012.
The 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 - based on her dissertation).
Journal of Time Series Analysis,
29, Issue 5, 811-833, 2008.
The Matlab programs are also available as a pdf file for ease of reading and as an ascii file.
- 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 - based on her dissertation).
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). mix.pdf.
Discrimination and Classification of Nonstationary Time Series using the
SLEX Model (with H-Y Huang & H. Ombao - based on Huang's dissertation):
the tech report
(the tech report has detailed proofs). Journal of
the American Statistical Association, 99, 763-774, 2004.
The Matlab programs are also available here.
- Resampling in State Space Models (booty.pdf) Chapter 9 (pp. 171-202) of State Space and Unobserved Component Models: Theory and Applications. Cambridge University Press, 2004.
- 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
D.S. (1991). Walsh-Fourier analysis and its statistical applications (with
discussion). Journal of the American Statistical Association, 86,
wft.for Fortran program to calculate the finite Walsh transform.
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. Find
it here (JSTOR).
Here are the data files: group1 and group2; details are in the first file. 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
I've been asked for the data from this a few times, so here they are: slpmv1.dat.txt, slpmv2.dat.txt. The data files are similar to the sleep state data files with an additional column of the per minute number of movements.
- Shumway, R.H. & Stoffer, D.S. (1992). Dynamic linear models with switching. Journal of the American Statistical Association, 86, 763-769. dlmws.pdf
R.H. & Stoffer, D.S. (1982). An approach to time series smoothing and
forecasting using the EM algorithm. Journal of Time Series Analysis,
R code for the algorithm can be found at the website for the third editon of our text. We still get many requests for the tech report corresponding to this paper. Unfortunately, the tech reports are long gone (believe it or not, in those days people typed their papers using an IBM selectric typewriter with little balls that had to be changed for math symbols). Fortunately, the details of the proofs (in more depth than was given in the tech report) are presented in our text in Sections 6.2 to 6.4.
- 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
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.dat.txt, cpue2.dat.txt, cpue3.dat.txt, cpue4.dat.txt, cpue5.dat.txt
- 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
- ...some info for my med school friends.