Selected Papers:

Constrained Statistical Decisions in Evolving Environments,
by Elijah Gaioni, Dipak Dey, and Daniel Larose.
Journal of Intelligent Systems, to appear.

Evaluation of Port Access Devices,
by Brenda Nurse, M.D., and Daniel Larose.
In preparation.

A Bayesian Meta-Analysis of the Relationship Between Duration of Estrogen Exposure and Endometrial Cancer
 in Meta-Analysis in Medicine and Health Policy,  
D. Berry and D. Stangl, Eds., Marcel-Dekker, New York, 2000.
A Bayesian random-effect model is presented for combining exposure-response information from several studies examining possible association between estrogen exposure and endometrial cancer. A Wishart distribution is used to model the within-study dependence of the vector of log relative risks. Data from 17 published studies which provided exposure-response information were combined. Samples from the joint posterior were generated by the Gibbs sampler. Results indicate evidence of a significant exposure-response relationship between estrogen and endometrial cancer.

Bayesian Duration-Response Meta-Analysis of Estrogen and Endometrial Cancer
 Proceedings of the Section on Bayesian Statistical Science, American Statistical Association, Alexandria, Virginia, 1998.

Modeling Publication Bias Using Weighted Distributions in a Bayesian Framework     Computational Statistics and Data Analysis, Vol 26, 279-302, 1998.
Meta-analysis refers to the quantitative synthesis of evidence from a
set of related studies.  Inference based on naive use of meta-analysis may be erroneous, however, due to publication bias, the tendency of investigators or editors to base decisions regarding submission or acceptance of manuscripts for publication depending on the strength of the investigator's study findings. Weighted distributions are ideally suited to model this phenomenon, since the weight function is proportional to the probability that the observation (in this case, the results of a study) enters the record. Models induced by several competing weight functions are compared, using the education data of Hedges and Olkin (1985).  Here, such models are fit hierarchically from a Bayesian perspective using noninformative priors in order to let the data drive the inference. Several model selection criteria are used in the spirit of exploratory data analysis for the appropriate choice of the weight function.

Grouped Random Effects Models for Bayesian Meta-Analysis
Statistics in Medicine, Vol. 16, 1817-1829, 1997.

Weighted Distributions Viewed in the Context of Model Selection: a Bayesian Perspective
Test, The Journal of the Spanish Statistical Society, Vol. 5, 1, 227-246, 1996.
In Section 2, a Bayesian formulation of the weighted model is motivated and derived. Section 3 presents the general form of the Bayes factor for weighted vs. unweighted model selection, as well as a convenient computational form.  Section 4 discusses the behavior of the Bayes factor under known
weight functions for the one parameter exponential family, and a table of closed form expressions for the Bayes factor under conjugate prior. In Section 5, a Bayes factor form is derived for unknown weight functions followed by an example. Section 6 outlines alternatives when closed form
expressions for the Bayes factor turn out to be analytically intractable. Section 7 discusses mixture distributions as weighted distributions and Section 8 illustrates the use of the Monte
Carlo estimate of the Bayes factor presented in Section 6.

Modeling Heterogeneity and Extraneous Variation using Weighted Distributions
 Model-Oriented Data Analysis 4, Kitsos and Muller, eds, Physica Verlag, Heidelberg, Germany, 1995.