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Full-Text Articles in Statistical Models
Unification Of Variance Components And Haseman-Elston Regression For Quantitative Trait Linkage Analysis, Wei-Min Chen, Karl W. Broman, Kung-Yee Liang
Unification Of Variance Components And Haseman-Elston Regression For Quantitative Trait Linkage Analysis, Wei-Min Chen, Karl W. Broman, Kung-Yee Liang
Johns Hopkins University, Dept. of Biostatistics Working Papers
Two of the major approaches for linkage analysis with quantitative traits in humans include variance components and Haseman-Elston regression. Previously, these have been viewed as quite separate methods. We describe a general model, fit by use of generalized estimating equations (GEE), for which the variance components and Haseman-Elston methods (including many of the extensions to the original Haseman-Elston method) are special cases, corresponding to different choices for a working covariance matrix. We also show that the regression-based test of Sham et al.(2002) is equivalent to a robust score statistic derived from our GEE approach. These results have several important implications. …
A Nested Unsupervised Approach To Identifying Novel Molecular Subtypes, Elizabeth Garrett, Giovanni Parmigiani
A Nested Unsupervised Approach To Identifying Novel Molecular Subtypes, Elizabeth Garrett, Giovanni Parmigiani
Johns Hopkins University, Dept. of Biostatistics Working Papers
In classification problems arising in genomics research it is common to study populations for which a broad class assignment is known (say, normal versus diseased) and one seeks to find undiscovered subclasses within one or both of the known classes. Formally, this problem can be thought of as an unsupervised analysis nested within a supervised one. Here we take the view that the nested unsupervised analysis can successfully utilize information from the entire data set for constructing and/or selecting useful predictors. Specifically, we propose a mixture model approach to the nested unsupervised problem, where the supervised information is used to …