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On Ecological Aspects of Dynamics for Zero Slope Regression for Water Pollution in Chile
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SYSNO ASEP 0504430 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title On Ecological Aspects of Dynamics for Zero Slope Regression for Water Pollution in Chile Author(s) Stehlík, M. (AT)
Núñez Soza, L. (CL)
Fabián, Zdeněk (UIVT-O) SAI, RID
Jiřina, Marcel (UIVT-O) SAI, RID
Jordanova, P. (BG)
Arancibia, S. C. (CL)
Kiselák, J. (SK)Source Title Stochastic Analysis and Applications. - : Taylor & Francis - ISSN 0736-2994
Roč. 37, č. 4 (2019), s. 574-601Number of pages 28 s. Language eng - English Country US - United States Keywords robust regression ; score regression ; non-normal distribution of residuals ; boron ; arsenic Subject RIV BB - Applied Statistics, Operational Research OECD category Statistics and probability R&D Projects EF16_013/0001787 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Method of publishing Limited access Institutional support UIVT-O - RVO:67985807 UT WOS 000466278700001 EID SCOPUS 85064162936 DOI 10.1080/07362994.2019.1592692 Annotation Zero slope regression is an important problem in chemometrics, ranging from challenges of intercept-bias and slope ‘corrections’ in spectrometry, up to analysis of administrative data on chemical pollution in water in the region of Arica and Parinacota. Such issue is really complex and it integrates problems of optimal design, symmetry of errors, stabilization of the variability of estimators, dynamical system for errors up to an administrative data challenges. In this article we introduce a realistic approach to zero slope regression problem from dynamical point of view. Linear regression is a widely used approach for data fitting under assumption of normally distributed residuals. Many times non-normal residuals are observed and also theoretically justified. Our solution to such problem uses the recently introduced inference function called score function of distribution. As a minimization criterion, the minimum information of residuals criterion is used. The score regression appears to be a direct generalization of the least-squares regression for an arbitrary known (believed) distribution of residuals. The score estimation is also distribution sensitive version of M-estimation. The capability of the method is demonstrated by water pollution data examples. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2020 Electronic address http://dx.doi.org/10.1080/07362994.2019.1592692
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