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  • This contribution extends the procedure introduced in J.Theor.Biol. 258(3) 437-443 (2009) combining parametric and non-parametric estimation of hazard function for cancer patients has been developed. The procedure utilizes kernel smoothing of hazard function to identify parameters of deterministic model. The poster presents methods for estimation of roots of the first and the second derivatives of hazard function and compares the estimates with the roots of the identified parametric form of the hazard function. The mentioned roots indicate time of maximal risk and that of the most rapid risk change. The procedure is applied to the breast cancer and to the leukemia data.
  • This contribution extends the procedure introduced in J.Theor.Biol. 258(3) 437-443 (2009) combining parametric and non-parametric estimation of hazard function for cancer patients has been developed. The procedure utilizes kernel smoothing of hazard function to identify parameters of deterministic model. The poster presents methods for estimation of roots of the first and the second derivatives of hazard function and compares the estimates with the roots of the identified parametric form of the hazard function. The mentioned roots indicate time of maximal risk and that of the most rapid risk change. The procedure is applied to the breast cancer and to the leukemia data. (en)
Title
  • Change points detection of a smooth estimate of a hazard function
  • Change points detection of a smooth estimate of a hazard function (en)
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  • Change points detection of a smooth estimate of a hazard function
  • Change points detection of a smooth estimate of a hazard function (en)
skos:notation
  • RIV/00216224:14310/10:00044683!RIV11-GA0-14310___
http://linked.open...avai/riv/aktivita
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  • P(GAP201/10/1032), P(LC06024), V
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  • 250279
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  • RIV/00216224:14310/10:00044683
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  • hazard function; kernel smoothing; zero points of derivative; deterministic model (en)
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  • [5ABB05BC7E75]
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  • Pospíšil, Zdeněk
  • Horová, Ivanka
  • Zelinka, Jiří
http://localhost/t...ganizacniJednotka
  • 14310
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