Incorporating expert knowledge when estimating parameters of the proportional hazards model

This paper presents a methodology that can use both experts' knowledge and statistical data to estimate the parameters of PHM. The knowledge elicitation process is based on case analyses and comparisons. This method results in a set of inequalities which in turn define a feasible space for the values of the parameters of PHM. By sampling from the feasible space the empirical prior distribution can be estimated. Then, using the Bayes rule and statistical data the posterior distribution can be obtained. The technique described in this paper has been tested several times in laboratory experiments and real industrial cases and have shown very promising results. Since PHM has been applied in many areas such as medical science, finance, organizational demography, etc we believe that the results of this research have wide applicability

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