Exploring Semiparametric Models and Likelihood

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TITLE: Exploring Semiparametric Models and Likelihood

SPEAKER: Professor Kjell Doksum


I will give an overview of semiparametric models for two-sample and regression experiments. General frameworks and specific examples such as the Cox regression model will be considered. I will explore a model where the hazard rates of the treatment and control groups start out equal at the time the treatment is introduced and then diverges continuously as time increases. A semiparametric model with bounded random variables is also considered and it is shown that in this model empirical maximum likelihood estimates converge at a rate of n rather than usual rate root(n).

Various likelihoods in use for semiparametric models will be discussed and compared. This is joint work with Aki Ozeki.


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  • Created By:
    Anita Race
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  • Modified By:
    Fletcher Moore
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