|
|
- Robust backfitting
R code implementing a robust backfitting algorithm
for additive models
is available
here.
The corresponding manuscript is
Boente, G., Martínez, A. and Salibian-Barrera, M.
(2015) Robust estimators for additive models using
backfitting.
A Technical Report is available here.
- Robust
tests for linear regression models based on tau-estimates.
- R code to compute these tests and
estimate the corresponding p-values is available
here.
The corresponding manuscript is
Salibian-Barrera, M., Van Aelst, S. and Yohai, V.J. (2016) Robust tests
for linear regression models based on tau-estimates.
Computational Statistics and Data Analysis, 93, 436-455.
Available on-line here.
A preprint is available here.
- S-estimators for functional principal
component analysis - R code to compute
S-estimators for functional
principal components is available
here.
The corresponding paper is:
Boente, G. and Salibian-Barrera, M. (2015) S-estimators for
functional Principal Component Analysis,
Journal of the American Statistical Association,
110(511), 1100-1111, available on-line
here.
A preprint is available
here.
-
A robust and sparse K-means clustering algorithm.
An R package (called RSKC) to compute robust and sparse clusters
based on K-means can be found in
CRAN.
This work is based on
Kondo, Y.; Salibian-Barrera, M.; Zamar, R. (2016), RSKC: An R Package for
a Robust and Sparse K-Means Clustering Algorithm.
Journal of Statistical Software, 72(5), p. 1 - 26, Aug. 2016.
Available on-line here.
A preprint is available at arXiv:1201.6082v1.
Yumi's MSc thesis is available
here.
- An outlier robust fit for Generalized Additive Models - R
package to compute
a robust fit for Generalized Additive Models can be found
here.
The corresponding paper is: Azadeh, A. and Salibian-Barrera, M. (2011),
"An outlier-robust fit for Generalized Additive Models with applications to disease outbreak detection",
JASA, 106(494), 719-731.
- Penalised S-regression splines - R code to compute
penalised S-regression splines can be found
here.
The corresponding
paper is: Tharmaratnam, K., Claeskens, G., Croux, C. and Salibian-Barrera, M. (2010),
"S-estimation for penalised regression splines", Journal of Computational and Graphical Statistics, 19(3), 609-625.
- Fast tau -
MATLAB / OCTAVE and R
code to compute the tau-regression estimator
for linear regression
can be found
here.
The corresponding paper is:
Salibian-Barrera, M., Willems, G. and Zamar, R.H. (2008),
"The fast-tau estimator for regression". Journal of Computational and Graphical Statistics, 17, 659-682.
- Globally robust confidence intervals for
simple linear regression models -
R
code to compute globally robust confidence intervals
for the slope of a simple linear regression model
can be found
here.
The corresponding paper is:
Adrover, J. and Salibian-Barrera, M. (2010), "Globally robust confidence
intervals for simple linear regression", Computational Statistics and Data Analysis, 54(12), 2899-2913.
- Fast S -
Plain R & S-PLUS
code to compute the Fast-S estimator
for linear regression
can be found
here
This algorithm is also implemented in
the S- and MM-estimators in the
robustbase
package for R (see below).
The corresponding paper is:
Salibian-Barrera, M. and Yohai, V.J. (2006), "A fast algorithm for
S-regression estimates". Journal of Computational and Graphical Statistics 15, 414-427.
This is joint work
with Prof. Victor Yohai.
-
roblm -
The roblm R package for MM-regression estimators,
version 0.6.
The latest version of
this package
can be found in
CRAN. This package is not longer maintained. All its
functionality has been incorporated into the
robustbase
package (see below).
- robustbase - An R package implementing
state-of-the-art robust methods, particularly those described in the book
Robust statistics, theory and methods, by Maronna, Martin and Yohai,
Wiley, 2006.
Available in
CRAN. Currently maintained by Martin Maechler.
- R package for the Linear Grouping Algorithm
-
[Van Aelst, S., Wang, X., Zamar, R., and Zhu, R. Linear grouping using
orthogonal regression, Computational Statistics and Data Analysis (2006)].
It is able to use multiple CPUs
if available and it implements the GAP statistic
[Tibshirani, R., Walther, G. and Hastie, T. Estimating the number of clusters
in a data set via the gap statistic, JRSS B, (2001)].
It is available
here.
This is joint work with Justin Harrington.
|