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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JDS</journal-id>
      <journal-title-group>
        <journal-title>Journal of Data Science</journal-title>
      </journal-title-group>
      <issn pub-type="epub">1680-743X</issn>
      <issn pub-type="ppub">1680-743X</issn>
      <publisher>
        <publisher-name>SOSRUC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">140404</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.201610_14(4).0004</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>An Empirical Comparison of Block Bootstrap Methods: Traditional and Newer Ones</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Beyaztas</surname>
            <given-names>Beste H.</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Department of Statistics, Istanbul Medeniyet University, Istanbul, Turkey
; Department of Statistics, Dokuz Eylul University, Izmir, Turkey</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Firuzan</surname>
            <given-names>Esin</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">Department of Statistics, Dokuz Eylul University, Izmir, Turkey</aff>
      </contrib-group>
      <volume>14</volume>
      <issue>4</issue>
      <fpage>641</fpage>
      <lpage>656</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Abstract: In this study, we compared various block bootstrap methods in terms of parameter estimation, biases and mean squared errors (MSE) of the bootstrap estimators. Comparison is based on four real-world examples and an extensive simulation study with various sample sizes, parameters and block lengths. Our results reveal that ordered and sufficient ordered non-overlapping block bootstrap methods proposed by Beyaztas et al. (2016) provide better results in terms of parameter estimation and its MSE compared to conventional methods. Also, sufficient non-overlapping block bootstrap method and its ordered version have the smallest MSE for the sample mean among the others.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Block bootstrap</kwd>
        <kwd>bootstrap</kwd>
        <kwd>estimation</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
