Fall Seminar Series - November 11, 2004
University of Minnesota
School of Statistics
College of Liberal Arts
Multilevel
models with applications in genomics
Brian Caffo
Department of Biostatistics
Johns Hopkins University
Thursday, November 11, 2004
3:30 PM, 115
Ford Hall
Minneapolis, East Bank Campus
Social at 3:00 PM, 300 Ford Hall
Abstract
In
the simultaneous estimation of a large number of related
quantities, multilevel models provide a formal mechanism for
efficiently making use of the ensemble of the information in deriving
individual estimates. In this talk we present a novel and flexible
class of normal multilevel models, referred to as the ``power
conjugate family''. This family overcomes some of the severe
restrictions posed by standard conjugate normal models in describing
the relationship between sources of variations at different levels of
the model, while retaining attractive properties from the point of
view of computations. We show that estimates based on this
generalized family of conjugate distributions, outperform currently
prevalent methods in a range of plausible simulated experiments. Our
work was motivated by the analysis of data from high-throughput
experiments in genomics. We illustrate the use of the power conjugate
family on two such data sets, one of which gives an example where
uncritical application of standard conjugate models can produce poor
results.