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Some Robust Estimation Tools for Multivariate Models
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SYSNO ASEP 0449741 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Some Robust Estimation Tools for Multivariate Models Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID Source Title The 9th International Days of Statistics and Economics Conference Proceedings. - Praha : VŠE, 2015 / Löster T. ; Pavelka T. - ISBN 978-80-87990-06-3 Pages s. 713-722 Number of pages 10 s. Publication form Online - E Action International Days of Statistics and Economics /9./ Event date 10.09.2015-12.09.2015 VEvent location Prague Country CZ - Czech Republic Event type WRD Language eng - English Country CZ - Czech Republic Keywords robust data mining ; high-dimensional data ; cluster analysis ; outliers Subject RIV BB - Applied Statistics, Operational Research R&D Projects GA13-17187S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000380530000068 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2016 Electronic address http://msed.vse.cz/msed_2015/article/7-Kalina-Jan-paper.pdf
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