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Handbook of Metrology and Applications
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SYSNO ASEP 0565542 Document Type M - Monograph Chapter R&D Document Type Monograph Chapter Title Modern Approaches to Statistical Estimation of Measurements in the Location Model and Regression Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Vidnerová, Petra (UIVT-O) RID, SAI, ORCID
Soukup, Lubomír (UTIA-B) RID, ORCIDNumber of authors 3 Source Title Handbook of Metrology and Applications. - Singapore : Springer, 2022 / Aswal D. K. ; Yadav S. ; Takatsuji T. ; Rachakonda P. ; Kumar H. - ISBN 978-981-19-1550-5 Pages s. 1-22 Number of pages 22 s. Number of pages 980 Publication form Online - E Language eng - English Country SG - Singapore Keywords regression ; measurement error ; error propagation ; robustness ; Bayesian estimation OECD category Statistics and probability R&D Projects GA22-02067S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 ; UTIA-B - RVO:67985556 DOI 10.1007/978-981-19-1550-5_125-1 Annotation Metrology as the science about measurement is highly intertwined with statistical point estimation. Evaluating and controling uncertainty of measurements and analyzing them by means of exploratory data analysis (EDA) or predictive data mining requires to exploit advanced tools of statistical estimation. The main focus of the chapter is devoted to nonstandard approaches to the analysis of measurements in two fundamental models, namely, the location model and linear regression. Robust regression methods, which are resistant to the presence of outlying (anomalous) measurements, are discussed here. An illustration of their performance over a real dataset related to thyroid disease and a Monte Carlo simulation reveal here the least weighted squares estimator, which has remained quite neglected so far, outperforms much more renowned robust regression estimators in terms of the variability. Further, Bayesian estimation in the location model is revealed here to have the ability to incorporate previous measurements in a very intuitive way. Finally, the chapter gives a warning that linear regression performed on data contaminated by measurement errors yields biased estimates and requires specific estimation tools for the so-called measurement error model. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2024 Electronic address https://dx.doi.org/10.1007/978-981-19-1550-5_125-1
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