Prior robust empirical Bayes inference for large-scale data by conditioning on rank with application to microarray data

Parametric Empirical Bayes Methods for Microarray Data

Inference on Low-Rank Data Matrices with Applications to Microarray Data.

Probe-level microarray data are usually stored in matrices, where the row and column correspond to array and probe, respectively. Scientists routinely summarize each array by a single index as the expression level of each probe-set (gene). We examine the adequacy of a uni-dimensional summary for characterizing the data matrix of each probe-set. To do so, we propose a low-rank matrix model for t...

Empirical Bayes in the presence of exceptional cases, with application to microarray data

Empirical Bayes is a statistical approach for estimating a series of unknown parameters from a series of associated data observations. It provides an effective means to “borrow strength” from the ensemble of cases when making inference about each individual case. Such methods are ideally suited to genomic applications where data is collected for tens of thousands of genes simultaneously. Empiri...

Order-Restricted Inference with Linear Rank Statistics in Microarray Data

The classification of subjects with unknown distribution in a small sample size often involves order-restricted constraints in multivariate parameter setups. Those problems make the optimality of a conventional likelihood ratio based statistical inferences not feasible. Fortunately, Roy (1953) introduced union-intersection principle(UIP) which provides an alternative avenue. Multivariate linear...

statistical inference via empirical bayes approach for stationary and dynamic contingency tables

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