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DTSTAMP:20251006T235741Z
DESCRIPTION:Bayesian Variable Selection for Multi-Dimensional Semiparametri
 c Regression Models\n\n\n	Wednesday\, December 6\, 2017 2:00 pm– 3:0 0 pm\n
 	Purvis Hall\, 1020 Pine Ave. West\, Room 25\n\n\n	ALL ARE WELCOME\n	Abstract
 \n	\n	Humans are routinely exposed to mixtures of chemical and other environ
 mental factors\, making the quantification\n	of health effects associated w
 ith environmental mixtures a critical goal for establishing environmental 
 policy\n	sufficiently protective of human health. The quantification of the
  effects of exposure to an environmental mixture\n	poses several statistica
 l challenges. It is often the case that exposure to multiple pollutants in
 teract with each\n	other to affect an outcome.\n\n\n	Further\, the exposure 
 -response relationship between an outcome and some exposures\, such as som
 e metals\, can exhibit complex\, nonlinear forms\, since some exposures ca
 n be beneficial and detrimental at different ranges of exposure. To estima
 te the health effects of complex mixtures we propose\n	sparse tensor regres
 sion\, which uses tensor products of marginal basis functions to approxima
 te complex functions. We induce sparsity using multivariate spike and slab
  priors on the number of exposures that make up the tensor factorization. 
 We allow the number of components required to estimate the health effects 
 of multiple\n	pollutants to be unknown and estimate it from the data. The p
 roposed approach is interpretable\, as we can use the posterior probabilit
 ies of inclusion to identify pollutants that interact with each other.\n\n
 SEE THE PDF FOR MORE INFORMATION\n
DTSTART:20171206T190000Z
DTEND:20171206T200000Z
SUMMARY:SPECIAL SEMINAR: Bayesian Variable Selection for Multi-Dimensional 
 Semiparametric Regression Models
URL:/epi-biostat-occh/channels/event/special-seminar-b
 ayesian-variable-selection-multi-dimensional-semiparametric-regression-mod
 els-283034
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