A Generalized Order-restricted Inference Methodology for Selecting and Clustering Genes
College of Mathematics and System Science, Shenyang Normal University, Shenyang, 110034, P.R. China
a Corresponding author: firstname.lastname@example.org
There are many methods for selecting and clustering genes according to their time-course or dose-response profiles. These methods all necessitate the assumption of a constant variance through time or among dosages. This homoscedasticity assumption is, however, seldom satisfied in practice. In this paper, via the application of Shi’s (1994,1998) algorithms and a modified bootstrap procedure, we proposed a generalized order-restricted inference methodology for the same task without the homoscedasticity restriction. Simulation results show that our procedure can control the false positive rate and have some good qualities.
Key words: level probability / 2 E2 test / bootstrap sampling / PAVA algorithm
© Owned by the authors, published by EDP Sciences, 2016
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