MATEC Web of Conferences
Volume 44, 20162016 International Conference on Electronic, Information and Computer Engineering
|Number of page(s)||6|
|Section||Computer, Algorithm, Control and Application Engineering|
|Published online||08 March 2016|
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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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