Template:WebNotes/ALTAALTA Standard Folio Data PHazards: Difference between revisions
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Introduced by D. R. Cox, the proportional hazards (PH) model was developed in order to estimate the effects of different covariates influencing the times-to-failure of a system. The model has been widely used in the biomedical field, and recently there has been an increasing interest in its application in reliability engineering. In its original form, the model is non-parametric (i.e., no assumptions are made about the nature or shape of the underlying failure distribution. | Introduced by D. R. Cox, the proportional hazards (PH) model was developed in order to estimate the effects of different covariates influencing the times-to-failure of a system. The model has been widely used in the biomedical field, and recently there has been an increasing interest in its application in reliability engineering. In its original form, the model is non-parametric (i.e., no assumptions are made about the nature or shape of the underlying failure distribution). In ALTA, the proportional hazards model is included in its parametric form and can be used to analyze data with up to eight variables. | ||
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| [http://help. | | [http://help.synthesisplatform.net/weibull_alta9/index.htm#alta_standard_folios.htm V8/9 Help] or [http://help.synthesisplatform.net/weibull_alta10/index.htm#alta_standard_folios.htm V10 Help] | ||
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Latest revision as of 16:23, 20 April 2015
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| Proportional Hazards Model |
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Introduced by D. R. Cox, the proportional hazards (PH) model was developed in order to estimate the effects of different covariates influencing the times-to-failure of a system. The model has been widely used in the biomedical field, and recently there has been an increasing interest in its application in reliability engineering. In its original form, the model is non-parametric (i.e., no assumptions are made about the nature or shape of the underlying failure distribution). In ALTA, the proportional hazards model is included in its parametric form and can be used to analyze data with up to eight variables. |
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