- 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
- 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
A Note on Efficient Fitting of Stochastic Volatility Models (with Chen Gong).
Article and code available at GitHub.
AdaptSPEC: Adaptive Spectral Estimation
for Nonstationary Time Series (with Ori Rosen and Sally Wood).
Journal of the American Statistical Association, 1575-1589, 2012.
There is an R package for this called
Matlab programs are
also available from Ori:
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.
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.
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
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.
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).
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
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.
- The Spectral Envelope and Its Applications (with D.E. Tyler &
Statistical Science. 15(3): 224-253 (2000).
Stoffer, D.S. (1999).
Detecting common signals in multiple time series using the spectral envelope.
the American Statistical Association, 94, 1341-1356.
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
D.S., Tyler, D.E. & McDougall, A.J. (1993). Spectral analysis for categorical
time series: Scaling and the spectral envelope. Biometrika, 80,
D.S. (1991). Walsh-Fourier analysis and its statistical applications (with
discussion). Journal of the American Statistical Association, 86,
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.
Here are the data files: group1 and group2; details
are in the first file.
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
Journal of Time Series Analysis, 8, 449-467.
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:
The data files are similar to the sleep state data files with an additional column
of the per minute number of movements.
R.H. & Stoffer, D.S. (1992). Dynamic linear models with switching.
Journal of the American Statistical Association, 86, 763-769.
R.H. & Stoffer, D.S. (1982). An approach to time series smoothing and
forecasting using the EM algorithm. Journal of Time Series Analysis,
code for the algorithm can be found in the R package,
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.
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.
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,
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.
This material is discussed in Chapter 6 of
Shumway & Stoffer (2006).
code and examples can be found at
the website for the second editon of the text.
An implementation of the
algorithm can also be found in Gauss TSM.
- ...some info for my med school friends.