Presence of outliers in chemical data affects all least squares models, which are extensively used in chemometrics for data exploration and modeling. Therefore, more and more attention is paid to the so-called robust models and robust statistics that aim to construct models and estimates describing well data majority. Moreover, construction of robust models allows identifying outlying observations. The outliers identification is not only essential for a proper modeling but also for understanding the reasons for unique character of the outlying sample.
In this paper some basic concepts of robust techniques are presented and their usefulness in…(more)
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%0 Journal Article
%1 Daszykowski2007203
%A Daszykowski, M.
%A Kaczmarek, K.
%A Heyden, Y. Vander
%A Walczak, B.
%D 2007
%J Chemometrics and Intelligent Laboratory Systems
%K multivariate pca projection-pursuit robust statistics
%N 2
%P 203 - 219
%R DOI: 10.1016/j.chemolab.2006.06.016
%T Robust statistics in data analysis -- A review: Basic concepts
%U http://www.sciencedirect.com/science/article/B6TFP-4KRY8HP-2/2/32ce2f0fc2f8f0ef62f5b71a3a7212b6
%V 85
%X Presence of outliers in chemical data affects all least squares models, which are extensively used in chemometrics for data exploration and modeling. Therefore, more and more attention is paid to the so-called robust models and robust statistics that aim to construct models and estimates describing well data majority. Moreover, construction of robust models allows identifying outlying observations. The outliers identification is not only essential for a proper modeling but also for understanding the reasons for unique character of the outlying sample.
In this paper some basic concepts of robust techniques are presented and their usefulness in chemometric data analysis is stressed.
@article{Daszykowski2007203,
abstract = {Presence of outliers in chemical data affects all least squares models, which are extensively used in chemometrics for data exploration and modeling. Therefore, more and more attention is paid to the so-called robust models and robust statistics that aim to construct models and estimates describing well data majority. Moreover, construction of robust models allows identifying outlying observations. The outliers identification is not only essential for a proper modeling but also for understanding the reasons for unique character of the outlying sample.
In this paper some basic concepts of robust techniques are presented and their usefulness in chemometric data analysis is stressed.},
added-at = {2010-03-06T01:19:57.000+0100},
author = {Daszykowski, M. and Kaczmarek, K. and Heyden, Y. Vander and Walczak, B.},
biburl = {https://www.bibsonomy.org/bibtex/27343293dceedaeedc7730fa507979645/vivion},
description = {ScienceDirect - Chemometrics and Intelligent Laboratory Systems : Robust statistics in data analysis — A review : : Basic concepts},
doi = {DOI: 10.1016/j.chemolab.2006.06.016},
interhash = {ea43d01182a7a8ec635eb6af5501c62b},
intrahash = {7343293dceedaeedc7730fa507979645},
issn = {0169-7439},
journal = {Chemometrics and Intelligent Laboratory Systems},
keywords = {multivariate pca projection-pursuit robust statistics},
number = 2,
pages = {203 - 219},
timestamp = {2010-06-03T10:17:08.000+0200},
title = {Robust statistics in data analysis -- A review: Basic concepts},
url = {http://www.sciencedirect.com/science/article/B6TFP-4KRY8HP-2/2/32ce2f0fc2f8f0ef62f5b71a3a7212b6},
volume = 85,
year = 2007
}