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Abstract Details
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Activity Number:
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524
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #301985 |
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Title:
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Semiparametric Estimation for Marginal and Small Data Expression: The Kidney Data Example
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Author(s):
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Norou Diawara*+
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Companies:
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Old Dominion University
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Address:
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, , 23529,
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Keywords:
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Bivariate distribution ;
Dirac delta ;
MCMC algorithm
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Abstract:
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The exponential distribution is one of the most used types of distribution because of its importance in many lifetime applications and its properties. So is its bivariate form. Simply used, there can be limitations especially for a heterogeneous type population. A bivariate exponential model with exponential marginal densities, correlated via random variables and with finite probability of simultaneous occurrence. We include the presence of covariate information through a linear relationship, a special case of which was considered by Marshall and Olkin. We extend this model to the bivariate Erlang type distributions with same shape parameter. Illustration with real kidney data is presented. Differences in the kidney disease types are shown in a less tedious method than what has been previously used.
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Authors who are presenting talks have a * after their name.
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