1. Mixtures and Hierarchical Mixtures of Cox Experts
The goal during the present grant period will be to study and extend a
mixture model which combines features of the usual Cox proportional
hazards model with those of mixtures-of-experts. The first subaim will be
to compare the mixture of Cox experts approach to spline-based methods.
The second subaim is to develop and study a Markov chain Monte Carlo
approach to inference for the mixture of Cox experts. The third subaim
will be to develop, study and apply a hierarchical mixture of Cox experts
2. Semiparametric Bayesian Inference for Regression models
Seifu, Severini and Tanner (1997) present a model for Bayesian inference
in a linear model with independent and identically distributed errors that
does not require the specification of parametric family of densities for
the error distribution. The focus of this specific aim is to extend this
work to the censored regression case. Both the one-sample and regression
cases will be considered. This new methodology for censored data problem
will be examined via stimulation studies and validated using real data.
3. Approximate Monte Carlo Conditional Inference
Kolassa and Tanner (1997) present an algorithm for approximate Frequentist
conditional inference on two or more parameters. The method makes use of
the double saddle point approximations of Skovgaard (1987) to the
conditional cumulative distribution function of a sufficient statistic
given the remaining sufficient statistics. This approximation is then used
in conjunction with non-iterative simulation methods to generate a sample
from the distribution that approximates the joint distribution of the
sufficient statistics associated with the parameters of interest
conditional on the observed values of the sufficient statistics associated
with the nuisance parameters. The focus of this specific aim is to further
study and apply the algorithm of Kolassa and Tanner (1997) to general
problems in biostatistics. The algorithm will be applied to situations
such as general hierarchical log-linear models for multiway contingency
tables and logistic regression. The algorithm will be compared via
simulation and on real data sets to alternative methods for exact
conditional inference. The methodology will be improved by replacing the
Skovgaard approximation with a higher-order approximation due to Kolassa
(1996). This methodology will also be applied to a variety of models in
the generalized linear model family for which no competing exact or
higher-order methods are available.
If you are accessing this page during weekend or evening hours, the database may currently be offline for maintenance and should operational within a few hours. Otherwise, we have been notified of this error and will be addressing it immediately.
Please contact us
if this error persists.
We apologize for the inconvenience.
- The DCCPS Team.