- Email: [email protected]
Special Issue on Advances in Data Mining and Robust Statistics
Computational Statistics and Data Analysis 93 (2016) 388–389
Contents lists available at ScienceDirect
Computational Statistics and Data Analysis journal homepage: www.elsevier.com/locate/csda
Editorial
Special Issue on Advances in Data Mining and Robust Statistics
Recent development of information and network technologies produces data sets with sizes beyond the ability of commonly-used data analysis techniques. Big data sizes are a constantly moving target ranging from a few dozen terabytes to many petabytes of data in a single data set. The aim of data mining is to extract knowledge from the very large datasets, to gain insights about the data and complement statistical models. This special issue focuses on the interface between data mining and statistical modelling, with special emphasis on robust statistics. There were 58 submissions from which 7 have been accepted. We hope the papers in this special issue can invite more attention on recent techniques of data mining and robust methods. Cui et al. (2016) propose an efficient accelerated proximal gradient algorithm for sparse estimation of high-dimensional correlation. Tarr et al. (2016) investigated a robust estimation method for covariance and precision matrices under cellwise contamination. Alfons et al. (2016) considered an extension of the popular least angle regression procedure to groupwise variable selection. Salibian-Barrera et al. (2016) propose robust tests for linear regression models based on τ -estimate. Kirschstein et al. (2016) propose a consistent direct multivariate mode estimation procedure, called minimum volume peeling. Hämäläinen (2016) proposes a family of tight upper bounds for fast approximation of Fisher’s exact test. Martinez and Gray (2016) propose noise peeling methods to improve boosting algorithms. The Guest Editors are grateful to the authors how have submitted their papers to the special issue and the referees who had reviewed the papers in a timely manner. Finally, we would like to thank the Editor, Erricos Kontoghiorghes, for his support with this issue. References Alfons, A., Croux, C., Gelper, S., 2016. Robust groupwise least angle regression. Comput. Statist. Data Anal. 93, 421–435. CSDA-D-14-00541R2 SI: Advances in Data Mining and Robust Statistics. Cui, Y., Leng, C., Sun, D., 2016. Sparse estimation of high-dimensional correlation matrices. Comput. Statist. Data Anal. 93, 390–403. CSDA-D-13-01218R2 SI: Advances in Data Mining and Robust Statistics. Hämäläinen, W., 2016. New upper bounds for tight and fast approximation of Fisher’s exact test in dependency rule mining. Comput. Statist. Data Anal. 93, 469–482. CSDA-D-14-00690R1 SI: Advances in Data Mining and Robust Statistics. Kirschstein, T., Liebscher, S., Porzio, G.C., Ragozini, G., 2016. Minimum volume peeling: A robust nonparametric estimator of the multivariate mode. Comput. Statist. Data Anal. 93, 456–468. CSDA-D-14-00606R3 SI: Advances in Data Mining and Robust Statistics. Martinez, W., Gray, J.B., 2016. Noise peeling methods to improve boosting algorithms. Comput. Statist. Data Anal. 93, 483–497. CSDA-D-14-00515R3 SI: Advances in Data Mining and Robust Statistics. Salibian-Barrera, M., Van Aelst, S., Yohai, V.J., 2016. Robust tests for linear regression models based on image-estimates. Comput. Statist. Data Anal. 93, 436–455. CSDA-D-14-00562R1 SI: Advances in Data Mining and Robust Statistics. Tarr, G., Müller, S., Weber, N.C., 2016. Robust estimation of precision matrices under cellwise contamination. Comput. Statist. Data Anal. 93, 404–420. CSDA-D-14-00367R2 SI: Advances in Data Mining and Robust Statistics.
Michael W. Berry University of Tennessee, USA E-mail address: [email protected]. Jung Jin Lee Soongsil University, Republic of Korea E-mail address: [email protected]. http://dx.doi.org/10.1016/j.csda.2015.09.004 0167-9473/© 2015 Published by Elsevier B.V.
Editorial / Computational Statistics and Data Analysis 93 (2016) 388–389
389
Giovanni Montana Imperial College London, UK E-mail address: [email protected]. Stefan Van Aelst Ghent University, Belgium E-mail address: [email protected]. Ruben H. Zamar University of British Columbia, Canada E-mail address: [email protected]. Available online 8 October 2015
Contents lists available at ScienceDirect
Computational Statistics and Data Analysis journal homepage: www.elsevier.com/locate/csda
Editorial
Special Issue on Advances in Data Mining and Robust Statistics
Recent development of information and network technologies produces data sets with sizes beyond the ability of commonly-used data analysis techniques. Big data sizes are a constantly moving target ranging from a few dozen terabytes to many petabytes of data in a single data set. The aim of data mining is to extract knowledge from the very large datasets, to gain insights about the data and complement statistical models. This special issue focuses on the interface between data mining and statistical modelling, with special emphasis on robust statistics. There were 58 submissions from which 7 have been accepted. We hope the papers in this special issue can invite more attention on recent techniques of data mining and robust methods. Cui et al. (2016) propose an efficient accelerated proximal gradient algorithm for sparse estimation of high-dimensional correlation. Tarr et al. (2016) investigated a robust estimation method for covariance and precision matrices under cellwise contamination. Alfons et al. (2016) considered an extension of the popular least angle regression procedure to groupwise variable selection. Salibian-Barrera et al. (2016) propose robust tests for linear regression models based on τ -estimate. Kirschstein et al. (2016) propose a consistent direct multivariate mode estimation procedure, called minimum volume peeling. Hämäläinen (2016) proposes a family of tight upper bounds for fast approximation of Fisher’s exact test. Martinez and Gray (2016) propose noise peeling methods to improve boosting algorithms. The Guest Editors are grateful to the authors how have submitted their papers to the special issue and the referees who had reviewed the papers in a timely manner. Finally, we would like to thank the Editor, Erricos Kontoghiorghes, for his support with this issue. References Alfons, A., Croux, C., Gelper, S., 2016. Robust groupwise least angle regression. Comput. Statist. Data Anal. 93, 421–435. CSDA-D-14-00541R2 SI: Advances in Data Mining and Robust Statistics. Cui, Y., Leng, C., Sun, D., 2016. Sparse estimation of high-dimensional correlation matrices. Comput. Statist. Data Anal. 93, 390–403. CSDA-D-13-01218R2 SI: Advances in Data Mining and Robust Statistics. Hämäläinen, W., 2016. New upper bounds for tight and fast approximation of Fisher’s exact test in dependency rule mining. Comput. Statist. Data Anal. 93, 469–482. CSDA-D-14-00690R1 SI: Advances in Data Mining and Robust Statistics. Kirschstein, T., Liebscher, S., Porzio, G.C., Ragozini, G., 2016. Minimum volume peeling: A robust nonparametric estimator of the multivariate mode. Comput. Statist. Data Anal. 93, 456–468. CSDA-D-14-00606R3 SI: Advances in Data Mining and Robust Statistics. Martinez, W., Gray, J.B., 2016. Noise peeling methods to improve boosting algorithms. Comput. Statist. Data Anal. 93, 483–497. CSDA-D-14-00515R3 SI: Advances in Data Mining and Robust Statistics. Salibian-Barrera, M., Van Aelst, S., Yohai, V.J., 2016. Robust tests for linear regression models based on image-estimates. Comput. Statist. Data Anal. 93, 436–455. CSDA-D-14-00562R1 SI: Advances in Data Mining and Robust Statistics. Tarr, G., Müller, S., Weber, N.C., 2016. Robust estimation of precision matrices under cellwise contamination. Comput. Statist. Data Anal. 93, 404–420. CSDA-D-14-00367R2 SI: Advances in Data Mining and Robust Statistics.
Michael W. Berry University of Tennessee, USA E-mail address: [email protected]. Jung Jin Lee Soongsil University, Republic of Korea E-mail address: [email protected]. http://dx.doi.org/10.1016/j.csda.2015.09.004 0167-9473/© 2015 Published by Elsevier B.V.
Editorial / Computational Statistics and Data Analysis 93 (2016) 388–389
389
Giovanni Montana Imperial College London, UK E-mail address: [email protected]. Stefan Van Aelst Ghent University, Belgium E-mail address: [email protected]. Ruben H. Zamar University of British Columbia, Canada E-mail address: [email protected]. Available online 8 October 2015