Robust statistics in data analysis -- A review: Basic concepts

by: M Daszykowski, K Kaczmarek, Vander, B Walczak

Chemometrics and Intelligent Laboratory Systems, Vol. 85, No. 2. (15 February 2007), pp. 203-219.

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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.

@article{citeulike:3013851, 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.}, author = {Daszykowski, M. and Kaczmarek, K. and Vander and Walczak, B. }, citeulike-article-id = {3013851}, doi = {10.1016/j.chemolab.2006.06.016}, journal = {Chemometrics and Intelligent Laboratory Systems}, keywords = {statistics}, month = {February}, number = {2}, pages = {203--219}, posted-at = {2008-07-17 09:21:57}, priority = {2}, title = {Robust statistics in data analysis -- A review: Basic concepts}, url = {http://dx.doi.org/10.1016/j.chemolab.2006.06.016}, volume = {85}, year = {2007} }

RIS record

TY - JOUR ID - citeulike:3013851 L3 - citeulike-article-id:3013851 TI - Robust statistics in data analysis -- A review: Basic concepts JF - Chemometrics and Intelligent Laboratory Systems VL - 85 IS - 2 SP - 203 EP - 219 N2 - 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. KW - statistics AU - Daszykowski, M AU - Kaczmarek, K AU - Vander AU - Walczak, B PY - 2007/02/15/ UR - http://dx.doi.org/10.1016/j.chemolab.2006.06.016 ER -

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