0565542 20240404124054.820221215235959.9 10.1007/978-981-19-1550-5_125-1 22 s. 980 E cav_un_epca*0565541978-981-19-1550-51-22SingaporeSpringer2022AswalD. K.YadavS.TakatsujiT.RachakondaP.KumarH. regression measurement error error propagation robustness Bayesian estimation cav_un_auth*0263018 Kalina Jan UIVT-O Oddělení strojového učení Department of Machine Learning Department of Machine Learning K Ústav informatiky AV ČR, v. v. i. cav_un_auth*0231277 Vidnerová Petra UIVT-O Oddělení strojového učení Department of Machine Learning Department of Machine Learning Ústav informatiky AV ČR, v. v. i. cav_un_auth*0101199 Soukup Lubomír UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. https://dx.doi.org/10.1007/978-981-19-1550-5_125-1 GA22-02067S GA ČR CZ cav_un_auth*0435776 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. 10000 10100 10103 2024 3 4 A 4a 4a 20231122151028.1 RVO:67985807 RVO:67985556 https://hdl.handle.net/11104/0337067 cav_un_auth*0339298 UTIA S 2022 Soubory v repozitáři: 0565542-afin.pdf, 0565542-a.pdf SCOPUS PUBMED WOS cav_un_epca*0565541 Handbook of Metrology and Applications Springer 2022 Singapore 1 22 978-981-19-1550-5 Aswal D. K. 340 Yadav S. 340 Takatsuji T. 340 Rachakonda P. 340 Kumar H. 340