205 citations · 247 across the 5 of their papers we have counts for
8 papers
Non-Parametric Cluster Significance Testing with Reference to a Unimodal Null Distribution
Erika S. Helgeson, Eric Bair
Cluster analysis is an unsupervised learning strategy that can be employed to identify subgroups of observations in data sets of unknown structure. This strategy is particularly us…
Biclustering Via Sparse Clustering
Qian Liu, Guanhua Chen, Michael R. Kosorok +1
In many situations it is desirable to identify clusters that differ with respect to only a subset of features. Such clusters may represent homogeneous subgroups of patients with a…
Semi-supervised clustering methods
Eric Bair
Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics.…
Identification of significant features in DNA microarray data
Eric Bair
DNA microarrays are a relatively new technology that can simultaneously measure the expression level of thousands of genes. They have become an important tool for a wide variety of…
Cross-Validation for Nonlinear Mixed Effects Models
Emily Colby, Eric Bair
Cross-validation is frequently used for model selection in a variety of applications. However, it is difficult to apply cross-validation to mixed effects models (including nonlinea…
Parameter estimation in Cox models with missing failure indicators and the OPPERA study
Naomi Brownstein, Jianwen Cai, Gary Slade +1
In a prospective cohort study, examining all participants for incidence of the condition of interest may be prohibitively expensive. For example, the "gold standard" for diagnosing…