• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.421-429
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• 2008

We consider a general semiparametric additive risk model that consists of three components. They are parametric, purely and smoothly nonparametric components. In parametric component, time dependent term is known up to proportional constant. In purely nonparametric component, time dependent term is an unknown function, and time dependent term in smoothly nonparametric component is an unknown but smoothly function. As an estimation method of this model, we use the weighted least square estimation by Huffer and McKeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.401-409
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• 2006

We consider a generalized partly-parametric additive risk model which generalizes the partly parametric additive risk model suggested by McKeague and Sasieni (1994). As an estimation method of this model, we propose to use the weighted least square estimation, suggested by Huffer and McKeague (1991), for Aalen's additive risk model by a piecewise constant risk. We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least squares method.