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  <channel rdf:about="https://elib.bsu.by:443/handle/123456789/93226">
    <title>ЭБ Коллекция:</title>
    <link>https://elib.bsu.by:443/handle/123456789/93226</link>
    <description />
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        <rdf:li rdf:resource="https://elib.bsu.by:443/handle/123456789/93478" />
        <rdf:li rdf:resource="https://elib.bsu.by:443/handle/123456789/93477" />
        <rdf:li rdf:resource="https://elib.bsu.by:443/handle/123456789/93476" />
        <rdf:li rdf:resource="https://elib.bsu.by:443/handle/123456789/93475" />
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    <dc:date>2026-04-20T07:57:51Z</dc:date>
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  <item rdf:about="https://elib.bsu.by:443/handle/123456789/93478">
    <title>A Distance-Distance Plot for Diagnosing Multivariate Outliers</title>
    <link>https://elib.bsu.by:443/handle/123456789/93478</link>
    <description>Заглавие документа: A Distance-Distance Plot for Diagnosing Multivariate Outliers
Авторы: Willems, G.
Аннотация: We propose a diagnostic method that can be used whenever multiple outliers&#xD;
are identified by robust estimates for multivariate location and scatter. The&#xD;
main purpose is visualization of the multivariate data to help determine whether&#xD;
the detected outliers (a) form a separate cluster or (b) are isolated or randomly&#xD;
scattered (such as heavy tails compared with Gaussian). We make use of Mahalanobis&#xD;
distances in order to check for separation and to reveal additional aspects&#xD;
of the data structure. The method is especially useful if multivariate structure&#xD;
is not seen in bivariate scatterplots.</description>
    <dc:date>2007-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://elib.bsu.by:443/handle/123456789/93477">
    <title>Fuzzy Data and Statistical Modeling</title>
    <link>https://elib.bsu.by:443/handle/123456789/93477</link>
    <description>Заглавие документа: Fuzzy Data and Statistical Modeling
Авторы: Viertl, R.
Аннотация: Data are frequently not precise numbers but more or less non-precise, also&#xD;
called fuzzy. Before analyzing such data the mathematical description of fuzzy&#xD;
data is necessary. This is possible using fuzzy models. Based on this, descriptive&#xD;
data analysis as well as statistical modeling have to be adapted. Basic methods&#xD;
for this are described in this contribution.</description>
    <dc:date>2007-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://elib.bsu.by:443/handle/123456789/93476">
    <title>Nadaraya-Watson Density Estimation for Interval Censored Data</title>
    <link>https://elib.bsu.by:443/handle/123456789/93476</link>
    <description>Заглавие документа: Nadaraya-Watson Density Estimation for Interval Censored Data
Авторы: Stoimenova, E.
Аннотация: This paper is concerned with the nonparametric estimation of a density function when the data are incomplete due to interval censoring. The Nadaraya-&#xD;
Watson kernel density estimator is modified to allow description of such interval&#xD;
data.</description>
    <dc:date>2007-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://elib.bsu.by:443/handle/123456789/93475">
    <title>On Statistical Classification of Scientific Texts</title>
    <link>https://elib.bsu.by:443/handle/123456789/93475</link>
    <description>Заглавие документа: On Statistical Classification of Scientific Texts
Авторы: Rudzkis, R.; Balys, V.
Аннотация: The research considers the problem of classification of scientific texts. Models&#xD;
and methods based on stochastic distribution of scientific terms are discussed.&#xD;
The preliminar results of experimental study over real-world data are reported.</description>
    <dc:date>2007-01-01T00:00:00Z</dc:date>
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