bibtype	M - Monography Chapter
ARLID	0565542
utime	20240404124054.8
mtime	20221215235959.9
DOI	10.1007/978-981-19-1550-5_125-1
title (primary) (eng)	Modern Approaches to Statistical Estimation of Measurements in the Location Model and Regression
specification	page_count 22 s. book_pages 980 media_type E
serial	ARLID cav_un_epca*0565541 ISBN 978-981-19-1550-5 title Handbook of Metrology and Applications page_num 1-22 publisher place Singapore name Springer year 2022 editor name1 Aswal name2 D. K. editor name1 Yadav name2 S. editor name1 Takatsuji name2 T. editor name1 Rachakonda name2 P. editor name1 Kumar name2 H.
keyword	regression
keyword	measurement error
keyword	error propagation
keyword	robustness
keyword	Bayesian estimation
author (primary)	ARLID cav_un_auth*0263018 name1 Kalina name2 Jan institution UIVT-O full_dept (cz) Oddělení strojového učení full_dept (eng) Department of Machine Learning full_dept Department of Machine Learning garant K fullinstit Ústav informatiky AV ČR, v. v. i.
author	ARLID cav_un_auth*0231277 name1 Vidnerová name2 Petra institution UIVT-O full_dept (cz) Oddělení strojového učení full_dept Department of Machine Learning full_dept Department of Machine Learning fullinstit Ústav informatiky AV ČR, v. v. i.
author	ARLID cav_un_auth*0101199 name1 Soukup name2 Lubomír institution UTIA-B full_dept (cz) Zpracování obrazové informace full_dept Department of Image Processing department (cz) ZOI department ZOI full_dept Department of Image Processing fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	url https://dx.doi.org/10.1007/978-981-19-1550-5_125-1
cas_special	project project_id GA22-02067S agency GA ČR country CZ ARLID cav_un_auth*0435776 abstract (eng) 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. FORD0 10000 FORD1 10100 FORD2 10103 reportyear 2024 num_of_auth 3 mrcbC52 4 A 4a 4a 20231122151028.1 inst_support RVO:67985807 inst_support RVO:67985556 permalink https://hdl.handle.net/11104/0337067 cooperation ARLID cav_un_auth*0339298 name UTIA confidential S arlyear 2022 mrcbTft \nSoubory v repozitáři: 0565542-afin.pdf, 0565542-a.pdf mrcbU14 SCOPUS mrcbU24 PUBMED mrcbU34 WOS mrcbU63 cav_un_epca*0565541 Handbook of Metrology and Applications Springer 2022 Singapore 1 22 978-981-19-1550-5 mrcbU67 Aswal D. K. 340 mrcbU67 Yadav S. 340 mrcbU67 Takatsuji T. 340 mrcbU67 Rachakonda P. 340 mrcbU67 Kumar H. 340