
 Lau, K. SalibianBarrera, M. and Lampe, L. (2016).
Modulation recognition in the 868 MHz band using classification
trees and random forests. AEU  International Journal of Electronics and Communications
70(9), 13211328
 Kondo, Y., SalibianBarrera, M. and Zamar, R. H. (2016). A robust and
sparse Kmeans clustering algorithm. Journal of
Statistical Software, 72(5).
 Available online
here.
 A preprint is available at arXiv:1201.6082v1.
 The
corresponding R package (RSKC) is available from CRAN (a direct link is here).
 This work is based on Yumi's MSc thesis (available here).
 SalibianBarrera, M., Van Aelst, S. and Yohai, V.J. (2016) Robust
tests for linear regression models based on tauestimates.
Computational Statistics and Data Analysis, 93, 436455.
 Available online here.
 A preprint
is available here.
 R and MATLAB code
and scripts reproducing the example in the paper are available here.
 Boente, G., Martínez, A. and SalibianBarrera, M.
(2016) Robust estimators for additive models using
backfitting. Submitted.
 Boente, G. and SalibianBarrera, M. (2015) Sestimators for functional
principal component analysis. Journal of the American Statistical
Association, 110(511), 11001111.
 Available online here.
 An older
preprint is available here
and a new one here.
 R code
implementing the method and scripts reproducing the examples in the paper
are available here.
 Boente, G., SalibianBarrera, M. and Tyler, D. (2014) A
characterization of elliptical distributions and some optimality properties
of principal components for functional data. Journal of Multivariate
Analysis, 131, 254264.
 Christmann, A., SalibianBarrera, M. and van Aelst, S. (2013)
Qualitative Robustness of Bootstrap Approximations for Kernel Based
Methods. Chapter 16 of Robustness and Complex Data Structures, C.
Becker, S. Kuhnt, and R. Fried, Eds. Springer, Heidelberg, New York.
 Available online here. An older
and shorter version is available at arXiv:1111.1876.
 This work was
also presented at the 59th ISI World Statistics Congress in Hong Kong. The
proceedings paper can be found here.
 Azadeh, A. and SalibianBarrera, M. (2012). An outlierrobust fit for
Generalized Additive Models with applications to disease outbreak
detection. Journal of the American Statistical Association.
106(494), 719731.
 Tharmaratnam, K., Claeskens, G., Croux, C. and SalibianBarrera, M.
(2010). Sestimation for penalised regression splines. Journal of
Computational and Graphical Statistics, 19(3), 609625.
 Available online here.
 R code
implementing the method
and a script illustrating its use
are available here.
 Adrover, J. and SalibianBarrera, M. (2010). Globally robust
confidence intervals for simple linear regression. Computational
Statistics and Data Analysis, 54(12), 28992913.
 Omelka, M. and SalibianBarrera, M. (2010). Uniform asymptotics for
S and MMregression estimators. Annals of the Institute of Statistical
Mathematics, 62(5), 897927.
 Harrington, J. and SalibianBarrera, M. (2010). Finding approximate
solutions to combinatorial problems with very large data sets using BIRCH.
Computational Statistics and Data Analysis, 54, 655667.
 Available online here.
 An R package
implementing the method is available here.
 SalibianBarrera, M. and Wei, Y. (2008). Weighted
quantile regression with nonelliptically structured
covariates. The Canadian Journal
of Statistics, 36, 595611.
 SalibianBarrera, M. and Van Aelst, S. (2008).
Robust model selection using fast and robust bootstrap.
Computational Statistics and Data Analysis,
52, 51215135.
 Available online
here.
 R code implementing the method is available
here.
 SalibianBarrera, M. and Yohai, V.J. (2008). High breakdown point
robust regression with censored data. The
Annals of Statistics, 36, 118146.
 Available online here
and here.
 A related technical report is available here.

SalibianBarrera, M., Willems, G. and Zamar, R.H. (2008).
The fasttau estimator for regression.
Journal of Computational
and Graphical Statistics, 17, 659682.
 Available
online here.
 An earlier version of the same
paper, titled
“Computation of tauestimates for linear regresion”,
is available
here.
 R, MATLAB & OCTAVE code for this algorithm is
available here.
 SalibianBarrera, M. and Yohai, V.J. (2006).
A fast algorithm for Sregression estimates.
Journal of Computational
and Graphical Statistics 15,
414427.
 Available online here.
 Standalone R code for this algorithm is
available here.
 This algorithm is also implemented in the S and MMestimators in the
R package
robustbase.
 SalibianBarrera, M. (2006). The
asymptotics of MMestimators for linear
regression with fixed designs.
Metrika, 63, 283294.
 The paper is
available online
here.
 SalibianBarrera, M., Van Aels, S. and Willems, G.
(2008). Fast and robust bootstrap. Statistical
Methods and Applications
17, 4171.
 Farrell, P.J. and SalibianBarrera, M. (2006). A
comparison of several robust estimators for a finite population
mean. Invited paper for a volumen
on the occasion of Prof.
A. K. Md. Ehsanes Saleh's 75th birthday.
Journal of Statistical Studies
26, 2943.
 A preliminary (PDF)
version is here.
 SalibianBarrera, M. and Zamar, R.H. (2006).
Discussion on "Conditional growth charts" by Wei, Y.
and He, X. The Annals of Statistics, 34,
21132118.
 SalibianBarrera, M. (2006). Bootstrapping
MMestimators for linear
regression with fixed designs.
Statistics and Probability
Letters, 76, 12871297.
 SalibianBarrera, M., Van Aelst, S. and
Willems, G. (2006). PCA based on multivariate
MMestimators with fast and robust bootstrap.
Journal of the American
Statistical Association, 101, 11981211.
 Farrell, P.J., SalibianBarrera, M. and
Naczk, K. (2007). On tests for multivariate
normality and associated simulation studies.
Journal of Statistical
Computation and Simulation,
77, 10651080.
 A
preprint is available
here.
 The paper is available online
here.
 SalibianBarrera, M. (2005).
Estimating the pvalues of robust
tests for the linear model.
Journal of Statistical
Planning and Inference,
128, 241257.
Available online
here.
 SalibianBarrera, M. (2005). Recent
advances in globally robust inference
methods. Journal of Statistical
Research, 39, 139156.
 Adrover, J.G, SalibianBarrera, M. and Zamar, R.H. (2004).
Globally robust inference for the location and
simple
linear regression models.
Journal of Statistical Planning and
Inference, 119, 353375.
 SalibianBarrera, M. and Zamar, R.H. (2004).
Uniform asymptotics for robust location estimates when
the scale is unknown.
The Annals of
Statistics, 32, 14341447.
 SalibianBarrera, M. (2003). Fast
and stable bootstrap methods for robust
estimates.
Computing Science and Statistics,
34, 346359 (E. Wegman and A.
Braverman editors). Interface Foundation
of North America, Inc., Fairfax Station, VA.
 SalibianBarrera, M. and Zamar, R.H. (2002).
Bootstrapping robust
estimates of regression.
The Annals of Statistics, 30,
556582.
 Available online
here.
 R code implementing the method is available
here.
 Adrover, J., Berrendero, J.R.,
SalibianBarrera, M. and Zamar, R.H. (2002).
Globally Robust Inference (a review).
Estadistica.
Available in
PDF format.
 Maronna, R. A., SalibianBarrera, M. and Yohai, V. J. (2000).
Improving biasrobustness of regression
estimates through projections.
Statistics and Probability Letters.
47,
149158
 SalibianBarrera, M. (2000). Contributions to the
theory of robust inference. PhD Thesis. Department
of Statistics, University of British Columbia.
Available in
Postscript and
PDF formats.
 SalibianBarrera, M. (1998).
On Globally Robust Confidence Intervals for Regression Coefficients,
UBC Department of Statistics Technical Report #176.
Available as
postscript or as
HTML.
