Sufficient Dimension Reduction via Bayesian Mixture Modeling
نویسندگان
چکیده
منابع مشابه
Sufficient dimension reduction via bayesian mixture modeling.
Dimension reduction is central to an analysis of data with many predictors. Sufficient dimension reduction aims to identify the smallest possible number of linear combinations of the predictors, called the sufficient predictors, that retain all of the information in the predictors about the response distribution. In this article, we propose a Bayesian solution for sufficient dimension reduction...
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Observational studies assessing causal or non-causal relationships between an explanatory measure and an outcome can be complicated by hosts of confounding measures. Large numbers of confounders can lead to several biases in conventional regression based estimation. Inference is more easily conducted if we reduce the number of confounders to a more manageable number. We discuss use of sufficien...
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ژورنال
عنوان ژورنال: Biometrics
سال: 2010
ISSN: 0006-341X
DOI: 10.1111/j.1541-0420.2010.01501.x