Sven Serneels

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Christophe CrouxKU Leuven - Faculty of Economics and BusinessVerified email at kuleuven.be
Peter FilzmoserProfessor of Statistics, TU WienVerified email at tuwien.ac.at
Tim VerdonckProfessor Statistics and Data Science, UAntwerpVerified email at uantwerpen.be
Beata WalczakInstitute of Chemistry, University of Silesia in KatowiceVerified email at us.edu.pl
Michal DaszykowskiProfessor in chemometrics, Institute of Chemistry, University of Silesia in Katowice, PolandVerified email at us.edu.pl
Michiel DebruyneCredit Risk Modeling, DexiaVerified email at dexia.com
Emmanuel Jordy MenvoutaPhD researcher, KU LeuvenVerified email at dataroots.io
Jason B. ChoCornell UniversityVerified email at cornell.edu
David S. MattesonProfessor, Statistics & Data Science, Cornell UniversityVerified email at cornell.edu
Ricardo Antonio MaronnaProfesor Consulto de la Facultad de Ciencias Exactas, Universidad Nacional de La PlataVerified email at mate.unlp.edu.ar
Frank BlockhuysStructural Chemistry Group, University of AntwerpVerified email at uantwerpen.be
Ivana StanimirovaUniversity of Silesia in Katowice, Institute of chemistryVerified email at us.edu.pl
Arijit ChakrabortyPh.D. Student at the Department of Chemical Engineering, Columbia University in the City of New YorkVerified email at columbia.edu
Venkat VenkatasubramanianSamuel Ruben-Peter G. Viele Professor of Engineering, Columbia UniversityVerified email at columbia.edu
Pieter SegaertUniversity of AntwerpVerified email at telenet.be

Sven Serneels

Technology Director - Data Science, Aspen Technology

Verified email at aspentech.com

Big Data Analytics Chemometrics Quantitative Finance Machine Learning Time Series Analysis


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TOMCAT: A MATLAB toolbox for multivariate calibration techniques M Daszykowski, S Serneels, K Kaczmarek, P Van Espen, C Croux, ... Chemometrics and intelligent laboratory systems 85 (2), 269-277, 2007	239	2007
Partial robust M-regression S Serneels, C Croux, P Filzmoser, PJ Van Espen Chemometrics and Intelligent Laboratory Systems 79 (1-2), 55-64, 2005	239	2005
Principal component analysis for data containing outliers and missing elements S Serneels, T Verdonck Computational Statistics & Data Analysis 52 (3), 1712-1727, 2008	180	2008
Spatial sign preprocessing: a simple way to impart moderate robustness to multivariate estimators S Serneels, E De Nolf, PJ Van Espen Journal of Chemical Information and Modeling 46 (3), 1402-1409, 2006	72	2006
Robust continuum regression S Serneels, P Filzmoser, C Croux, PJ Van Espen Chemometrics and Intelligent Laboratory Systems 76 (2), 197-204, 2005	55	2005
Sparse partial robust M regression I Hoffmann, S Serneels, P Filzmoser, C Croux Chemometrics and Intelligent Laboratory Systems 149, 50-59, 2015	52	2015
Robust multivariate methods in chemometrics P Filzmoser, S Serneels, R Maronna, C Croux Comprehensive Chemometrics, 2nd Edition, Steven Brown, Roma Tauler and Beata …, 2020	47*	2020
Calculation of PLS prediction intervals using efficient recursive relations for the Jacobian matrix S Serneels, P Lemberge, PJ Van Espen Journal of Chemometrics: A Journal of the Chemometrics Society 18 (2), 76-80, 2004	37	2004
Sparse and robust PLS for binary classification I Hoffmann, P Filzmoser, S Serneels, K Varmuza Journal of Chemometrics 30, 153-162, 2016	35	2016
Principal component regression for data containing outliers and missing elements S Serneels, T Verdonck Computational statistics & data analysis 53 (11), 3855-3863, 2009	33	2009
Detecting wash trading for nonfungible tokens S Serneels Finance Research Letters 52, 103374, 2023	32	2023
How to construct a multiple regression model for data with missing elements and outlying objects I Stanimirova, S Serneels, PJ Van Espen, B Walczak Analytica chimica acta 581 (2), 324-332, 2007	28	2007
Influence properties of partial least squares regression S Serneels, C Croux, PJ Van Espen Chemometrics and Intelligent Laboratory Systems 71 (1), 13-20, 2004	28	2004
Cellwise robust M regression P Filzmoser, S Höppner, I Ortner, S Serneels, T Verdonck Computational Statistics & Data Analysis 147, 106944, 2020	24	2020
Outlyingness: Which variables contribute most? M Debruyne, S Höppner, S Serneels, T Verdonck Statistics and Computing 29, 707-723, 2019	20	2019
Non-fungible token transactions: data and challenges JB Cho, S Serneels, DS Matteson Data Science in Science 2 (1), 2151950, 2023	19	2023
Robust multivariate methods: The projection pursuit approach P Filzmoser, S Serneels, C Croux, PJ Van Espen From Data and Information Analysis to Knowledge Engineering: Proceedings of …, 2006	19	2006
Bootstrap confidence intervals for trilinear partial least squares regression S Serneels, PJ Van Espen Analytica chimica acta 544 (1-2), 153-158, 2005	18	2005
Robustified least squares support vector classification M Debruyne, S Serneels, T Verdonck Journal of Chemometrics: A Journal of the Chemometrics Society 23 (9), 479-486, 2009	14	2009
Identification of micro-organisms by dint of the electronic nose and trilinear partial least squares regression S Serneels, M Moens, PJ Van Espen, F Blockhuys Analytica chimica acta 516 (1-2), 1-5, 2004	12	2004

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