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Some Robust Estimation Tools for Multivariate Models
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SYSNO ASEP 0449741 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Some Robust Estimation Tools for Multivariate Models Tvůrce(i) Kalina, Jan (UIVT-O) RID, SAI, ORCID Zdroj.dok. The 9th International Days of Statistics and Economics Conference Proceedings. - Praha : VŠE, 2015 / Löster T. ; Pavelka T. - ISBN 978-80-87990-06-3 Rozsah stran s. 713-722 Poč.str. 10 s. Forma vydání Online - E Akce International Days of Statistics and Economics /9./ Datum konání 10.09.2015-12.09.2015 Místo konání Prague Země CZ - Česká republika Typ akce WRD Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova robust data mining ; high-dimensional data ; cluster analysis ; outliers Vědní obor RIV BB - Aplikovaná statistika, operační výzkum CEP GA13-17187S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000380530000068 Anotace Standard procedures of multivariate statistics and data mining for the analysis of multivariate data are known to be vulnerable to the presence of outlying and/or highly influential observations. This paper has the aim to propose and investigate specific approaches for two situations. First, we consider clustering of categorical data. While attention has been paid to sensitivity of standard statistical and data mining methods for categorical data only recently, we aim at modifying standard distance measures between clusters of such data. This allows us to propose a hierarchical agglomerative cluster analysis for two-way contingency tables with a large number of categories, based on a regularized measure of distance between two contingency tables. Such proposal improves the robustness to the presence of measurement errors for categorical data. As a second problem, we investigate the nonlinear version of the least weighted squares regression for data with a continuous response. Our aim is to propose an efficient algorithm for the least weighted squares estimator, which is formulated in a general way applicable to both linear and nonlinear regression. Our numerical study reveals the computational aspects of the algorithm and brings arguments in favor of its credibility. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2016 Elektronická adresa http://msed.vse.cz/msed_2015/article/7-Kalina-Jan-paper.pdf
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