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  <titleInfo>
    <title>Predictive modeling in credit risk: a survival analysis case</title>
  </titleInfo>
  <name type="personal">
    <namePart>Omoga, Allan Anyona</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Samuel Mwalili (Prof.)</namePart>
  </name>
  <typeOfResource>text</typeOfResource>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">xxu</placeTerm>
    </place>
    <place>
      <placeTerm type="text">Nairobi</placeTerm>
    </place>
    <publisher>Strathmore University</publisher>
    <dateIssued>2017</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
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    <extent>viii,39p;</extent>
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  <abstract>Six survival analysis techniques are accessed by applying the techniques to a dataset consisting of 33,238 active credit facilities from a financial institution operating in Kenya. Namely, the Accelerated Failure Time (AFT) Models, Cox proportional hazard (PH) Model and the Mixture Cure Model (MCM) are considered in the comparisons. Evaluation of the techniques is conducted from a Statistical approach evaluation using the Area under the Curve (AUC) and financial evaluation using the annuity theory. The Cox Proportional Hazard (PH) and the Mixture cure model performs significantly well.
URI</abstract>
  <subject>
    <topic>Credit risk modeling</topic>
  </subject>
  <classification authority="lcc">HG3751.O46 2017</classification>
  <identifier type="uri">https://su-plus.strathmore.edu.ezproxy.library.strathmore.edu/handle/11071/5622</identifier>
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    <url>https://su-plus.strathmore.edu.ezproxy.library.strathmore.edu/handle/11071/5622</url>
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    <recordChangeDate encoding="iso8601">20260707210020.0</recordChangeDate>
    <recordIdentifier>311837</recordIdentifier>
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