
David S. Stoffer is a Professor in the
Department of Statistics
at (or near) the University of Pittsburgh.
Tele: [412] 6248496 Fax: [412] 6488814 Email: 
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
(Edition 3) by R.H. Shumway and D.S.
Stoffer. Springer Texts in Statistics, 2010.
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, 15751589, 2012.
AdaptSPEC.pdf
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, 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  based on her dissertation).
Journal of Time Series Analysis,
29, Issue 5, 811833, 2008.
svmm.pdf
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, 335345, 2007. multspec.pdf

A ResidualsBased Transition Model for Longitudinal Analysis with Estimation
in the Presence of Missing Data
(with T. KoruSengul  based on her dissertation).
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). mix.pdf.

Discrimination and Classification of Nonstationary Time Series using the
SLEX Model (with HY Huang & H. Ombao  based on Huang's dissertation):
the article
and
the tech report
(the tech report has detailed proofs). Journal of
the American Statistical Association, 99, 763774, 2004.
The Matlab programs are also available here.
 Resampling in State Space Models (booty.pdf) Chapter 9 (pp. 171202) 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, 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
wft.for Fortran program to calculate the finite Walsh transform.
 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 sleepstate cycling. Journal of the American Statistical
Association, 83, 954963. 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). 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
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, 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 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, 493500. gibbs.pdf
 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.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, 10241033.
boots.pdf
This material is discussed in Chapter 6 of Shumway & Stoffer (2006). Some R code and examples can be found at the website for the second editon of the text. An implementation of the algorithm for Gauss TSM users can be found here.
 ...some info for my med school friends.