Linked Life Data

20150656

Source:http://linkedlifedata.com/resource/pubmed/id/20150656

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-6-11
pubmed:abstractText
In various application areas, prior information is available about the direction of the effects of multiple predictors on the conditional response distribution. For example, in epidemiology studies of potentially adverse exposures and continuous health responses, one can typically assume a priori that increasing the level of an exposure does not lead to an improvement in the health response. Such an assumption can be formalized through a stochastic ordering assumption in each of the exposures, leading to a potentially large improvement in efficiency in nonparametric modeling of the conditional response distribution. This article proposes a Bayesian nonparametric approach to this problem based on characterizing the conditional response density as a Gaussian mixture, with the locations of the Gaussian means varying flexibly with predictors subject to minimal constraints to ensure stochastic ordering. Theoretical properties are considered and Markov chain Monte Carlo methods are developed for posterior computation. The methods are illustrated using simulation examples and a reproductive epidemiology application.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1468-4357
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
11
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
419-31
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Stochastically ordered multiple regression.
pubmed:affiliation
Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany. bornkamp@statistik.uni-dortmund.de
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't