Matías Salibián-Barrera [Publications & Code]



  • Lau, K. Salibian-Barrera, 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), 1321-1328
    • Available on-line here.

  • Kondo, Y., Salibian-Barrera, M. and Zamar, R. H. (2016). A robust and sparse K-means clustering algorithm. Journal of Statistical Software, 72(5).
    • Available on-line 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).

  • 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.
    • R and MATLAB code and scripts reproducing the example in the paper are available here.

  • Boente, G., Martínez, A. and Salibian-Barrera, M. (2016) Robust estimators for additive models using backfitting. Submitted.

  • Boente, G. and Salibian-Barrera, M. (2015) S-estimators for functional principal component analysis. Journal of the American Statistical Association, 110(511), 1100-1111.

  • Boente, G., Salibian-Barrera, 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, 254-264.

  • Christmann, A., Salibian-Barrera, 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 on-line 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 Salibian-Barrera, M. (2012). An outlier-robust fit for Generalized Additive Models with applications to disease outbreak detection. Journal of the American Statistical Association. 106(494), 719-731.

  • 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.
    • Available on-line here.
    • R code implementing the method and a script illustrating its use are available here.

  • Adrover, J. and Salibian-Barrera, M. (2010). Globally robust confidence intervals for simple linear regression. Computational Statistics and Data Analysis, 54(12), 2899-2913.

  • Omelka, M. and Salibian-Barrera, M. (2010). Uniform asymptotics for S- and MM-regression estimators. Annals of the Institute of Statistical Mathematics, 62(5), 897-927.

  • Harrington, J. and Salibian-Barrera, M. (2010). Finding approximate solutions to combinatorial problems with very large data sets using BIRCH. Computational Statistics and Data Analysis, 54, 655-667.
    • Available on-line here.
    • An R package implementing the method is available here.

  • Salibian-Barrera, M. and Wei, Y. (2008). Weighted quantile regression with non-elliptically structured covariates. The Canadian Journal of Statistics, 36, 595-611.

  • Salibian-Barrera, M. and Van Aelst, S. (2008). Robust model selection using fast and robust bootstrap. Computational Statistics and Data Analysis, 52, 5121-5135.
    • Available on-line here.
    • R code implementing the method is available here.

  • Salibian-Barrera, M. and Yohai, V.J. (2008). High breakdown point robust regression with censored data. The Annals of Statistics, 36, 118-146.
    • Available on-line here and here.
    • A related technical report is available here.

  • 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.
    • Available on-line here.
    • An earlier version of the same paper, titled “Computation of tau-estimates for linear regresion”, is available here.
    • R, MATLAB & OCTAVE code for this algorithm is available here.

  • Salibian-Barrera, M. and Yohai, V.J. (2006). A fast algorithm for S-regression estimates. Journal of Computational and Graphical Statistics 15, 414-427.
    • Available on-line here.
    • Stand-alone R code for this algorithm is available here.
    • This algorithm is also implemented in the S- and MM-estimators in the R package robustbase.

  • Salibian-Barrera, M. (2006). The asymptotics of MM-estimators for linear regression with fixed designs. Metrika, 63, 283-294.
    • The paper is available on-line here.

  • Salibian-Barrera, M., Van Aels, S. and Willems, G. (2008). Fast and robust bootstrap. Statistical Methods and Applications 17, 41-71.
    • Available on-line here.

  • Farrell, P.J. and Salibian-Barrera, 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, 29-43.
    • A preliminary (PDF) version is here.

  • Salibian-Barrera, M. and Zamar, R.H. (2006). Discussion on "Conditional growth charts" by Wei, Y. and He, X. The Annals of Statistics, 34, 2113-2118.

  • Salibian-Barrera, M. (2006). Bootstrapping MM-estimators for linear regression with fixed designs. Statistics and Probability Letters, 76, 1287-1297.
    • Available on-line here.

  • Salibian-Barrera, M., Van Aelst, S. and Willems, G. (2006). PCA based on multivariate MM-estimators with fast and robust bootstrap. Journal of the American Statistical Association, 101, 1198-1211.
    • Available on-line here.

  • Farrell, P.J., Salibian-Barrera, M. and Naczk, K. (2007). On tests for multivariate normality and associated simulation studies. Journal of Statistical Computation and Simulation, 77, 1065-1080.
    • A preprint is available here.
    • The paper is available on-line here.

  • Salibian-Barrera, M. (2005). Estimating the p-values of robust tests for the linear model. Journal of Statistical Planning and Inference, 128, 241-257.
    Available on-line here.

  • Salibian-Barrera, M. (2005). Recent advances in globally robust inference methods. Journal of Statistical Research, 39, 139-156.

  • Adrover, J.G, Salibian-Barrera, M. and Zamar, R.H. (2004). Globally robust inference for the location and simple linear regression models. Journal of Statistical Planning and Inference, 119, 353-375.

  • Salibian-Barrera, M. and Zamar, R.H. (2004). Uniform asymptotics for robust location estimates when the scale is unknown. The Annals of Statistics, 32, 1434-1447.

  • Salibian-Barrera, M. (2003). Fast and stable bootstrap methods for robust estimates. Computing Science and Statistics, 34, 346-359 (E. Wegman and A. Braverman editors). Interface Foundation of North America, Inc., Fairfax Station, VA.

  • Salibian-Barrera, M. and Zamar, R.H. (2002). Bootstrapping robust estimates of regression. The Annals of Statistics, 30, 556-582.
    • Available on-line here.
    • R code implementing the method is available here.

  • Adrover, J., Berrendero, J.R., Salibian-Barrera, M. and Zamar, R.H. (2002). Globally Robust Inference (a review). Estadistica.
    Available in PDF format.

  • Maronna, R. A., Salibian-Barrera, M. and Yohai, V. J. (2000). Improving bias-robustness of regression estimates through projections. Statistics and Probability Letters. 47, 149-158

  • Salibian-Barrera, M. (2000). Contributions to the theory of robust inference. PhD Thesis. Department of Statistics, University of British Columbia.
    Available in Postscript and PDF formats.

  • Salibian-Barrera, M. (1998). On Globally Robust Confidence Intervals for Regression Coefficients, UBC Department of Statistics Technical Report #176.
    Available as postscript or as HTML.





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