by J. S. Marron
This page is intended as a "table of
Each analysis has a teaser image to motivate the viewer to look at the
more detailed pages available on the links.
by P. Chaudhuri, J. S. Marron, J. C. Jiang, C. S. Kim, R. Z. Li, V. Rondonotti, J. de Uña Alvarez
SiZer was developed by Chaudhuri and Marron (1999, JASA, 94, 807-823), to address the problem of "which features in a smooth are important underlying structure, and which are unimportant sampling artifacts?"
Here is one screen shot, using the famous
data", that establishes the statistical significance of the "two
For a web based introduction to SiZer, more examples showing the
efficacy of SiZer, and discussion of other aspects, go here
2. CHETA (Curve Height at Event Time Analysis)
by T. Graves, J. S. Marron, A. Mockus and A. Glazner
This is a method for assessing statistical significance of connections between a curve and a point process. In particular, do the events of the point process occur more or less often depending on the height of the curve?
Our approach to this is based on evaluating the curve at the event times, and then comparing the resulting population with an appropriate "background" population.
Here is an interesting example from software
This provides strong evidence (p-value ~ 0.000001) that there is a
between rate of faults and perfective maintenance, in a large software
Here is another example from geology. This shows a
significant (p-value ~ 0.002) connection between glaciation and
To learn more about this example, and the CHETA
(linked page still under construction)
by J. S. Marron, R. Z. Li and C. A. Giuliani
Here is one trace of a repetitive human movement. Changes in
are clearly visible. Are there changes in frequency? Which
frequency changes are statistically significant? The first
is answered using Fourier analysis. The second question appears
need non-Fourier methods. An approach based on "movies" is
For the full story, go here.
Functional Data Analysis of Corneal Images
by N. Locantore, J. S. Marron, D. G. Simpson, N. Tripoli, J. T. Zhang and K. Cohen
The raw data are a set of corneal curvature images, as shown here. What is the underlying population structure of this set of data?
for an answer to this question (using movies in the data analysis), for
a new type of robust PCA, for the related paper to appear with
in Test, etc.
Significance in Scale Space
by F. Godtliebsen, J. S. Marron and P. Chaudhuri.
This addresses the same question as SiZer, see 1 above, but in the 2 dimensional case. Current versions work for 2d density estimation, and images (i.e. equally spaced regression). One problem in 2 dimensions is how to visualize the result. This is solved by using movies, where time represents the amount of smoothing (i.e. bandwidth). Another problem is that "derivative" becomes more complicated in 2d. This has motivated the development of some new visual paradigms.
Here is one frame of one of these movies
(but don't stop here!):
(Caution: this is only a "screen shot", so the buttons don't work. Click here to see this movie, but better is...)
for much more about this approach to finding structure in 2-d smoothing
contexts, including movies, papers, and downloadable software.
Marron's movie page