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<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.0 20120330//EN" "JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article"><front><journal-meta><journal-id journal-id-type="publisher-id">INFORMATICA</journal-id><journal-title-group><journal-title>Informatica</journal-title></journal-title-group><issn pub-type="epub">0868-4952</issn><issn pub-type="ppub">0868-4952</issn><publisher><publisher-name>VU</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">INF3407</article-id><article-id pub-id-type="doi">10.3233/INF-1992-3407</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research article</subject></subj-group></article-categories><title-group><article-title>Off-line estimation of dynamic systems parameters in the presence of outliers in observations</article-title></title-group><contrib-group><contrib contrib-type="Author"><name><surname>Pupeikis</surname><given-names>Rimantas</given-names></name><xref ref-type="aff" rid="j_INFORMATICA_aff_000"/></contrib><aff id="j_INFORMATICA_aff_000">Institute of Mathematics and Informatics, 2600 Vilnius, Akademijos St. 4, Lithuania</aff></contrib-group><pub-date pub-type="epub"><day>01</day><month>01</month><year>1992</year></pub-date><volume>3</volume><issue>4</issue><fpage>567</fpage><lpage>581</lpage><abstract><p>In the previous papers (Pupeikis, 1990; 1991; 1992) the problems of model oder determination and recursive estimation of dynamic systems parameters in the presence of outliers in observations have been considered. The aim of the given paper is the development, in such a case, of classical off-line algorithms for systems of unknown parameters estimation using batch processing of the stored data. An approach, based on a substitution of the corresponding values of the sample covariance and cross-covariance functions by their robust analogues in respective matrices and on a further application of the least square (LS) parameter estimation algorithm, is worked out. The results of numerical simulation by IBM PC/AT (Table 1, 2) are given.</p></abstract><kwd-group><label>Keywords</label><kwd>LS algorithm</kwd><kwd>covariance analysis</kwd><kwd>outlier</kwd><kwd>robustness</kwd></kwd-group></article-meta></front></article>