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  <titleInfo>
    <title>Time series analysis by state space methods</title>
  </titleInfo>
  <name type="personal">
    <namePart>J Durbin; S J Koopman</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>J Durbin; S J Koopman</namePart>
  </name>
  <typeOfResource>text</typeOfResource>
  <genre authority="">Time-series analysis-;MATHEMATICS -- Probability &amp; Statistics -- Time Series.</genre>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">xxu</placeTerm>
    </place>
    <place>
      <placeTerm type="text">Oxford</placeTerm>
    </place>
    <publisher>: Oxford University Press</publisher>
    <dateIssued>, 2012</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">print</form>
    <extent>xxi, 346 pages  : illustrations. ,25 cm</extent>
  </physicalDescription>
  <abstract>	
This is a comprehensive treatment of the state space approach to time series analysis. A distinguishing feature of state space time series models is that observations are regarded as made up of distinct components, which are each modelled separately.</abstract>
  <tableOfContents>	PART I: THE LINEAR STATE SPACE MODEL; 
PART II: NON-GAUSSIAN AND NONLINEAR STATE SPACE MODELS</tableOfContents>
  <note type="statement of responsibility">/J Durbin; S J Koopman</note>
  <subject>
    <topic>State-space methods</topic>
  </subject>
  <classification authority="ddc">519.55 DUR</classification>
  <identifier type="isbn">9780199641178</identifier>
  <identifier type="">Atlantic Publishers &amp; Distributors (P)Ltd. ;7/22,Ansari Road ,Darya Ganj,New Delhi-110002</identifier>
  <recordInfo>
    <recordCreationDate encoding="marc">180522</recordCreationDate>
    <recordChangeDate encoding="iso8601">20180522135744.0</recordChangeDate>
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