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Activity Number:
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233
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #305064 |
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Title:
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A Semiparametric Survival Model
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Author(s):
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Ying Zhang*+
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Companies:
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Merck Research Laboratories
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Address:
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305 Sumneytown Pike, North Wales, PA, 19454,
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Keywords:
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semiparametric model ; survival function ; hazard function ; mean squared error
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Abstract:
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Kouassi and Singh (1997) estimated the hazard function assuming it is a linear mixture of parametric and nonparametric components. The weight of each component is estimated by minimizing the mean squared error. This semiparametric model provides greater flexibility in the estimation of hazard function because the model will always assign more weight to the estimator that best fits the data. They show that when the parametric model holds, the proposed hazard estimator converges to the true hazard function at the same rate as the parametric hazard estimator; otherwise, it converges at the same rate as the nonparametric estimator. However, it remains unknown that if the corresponding survival function holds the same properties. This presentation will investigate and show that the semiparametric survival function holds the same properties as the semiparametric hazard estimator.
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