http://swrc.ontoware.org/ontology#Article
Inference on High-Dimensional Mean Vectors with Fewer Observations Than the Dimension
en
Yata Kazuyoshi
Aoshima Makoto
矢田 和善
青嶋 誠
We focus on inference about high-dimensional mean vectors when the sample size is much fewer than the dimension. Such data situation occurs in many areas of modern science such as genetic microarrays, medical imaging, text recognition, finance, chemometrics, and so on. First, we give a given-radius confidence region for mean vectors. This inference can be utilized as a variable selection of high-dimensional data. Next, we give a given-width confidence interval for squared norm of mean vectors. This inference can be utilized in a classification procedure of high-dimensional data. In order to assure a prespecified coverage probability, we propose a two-stage estimation methodology and determine the required sample size for each inference. Finally, we demonstrate how the new methodologies perform by using a microarray data set.
Methodology and computing in applied probability
14
3
459-476
2012-09
1387-5841
AA11603171
10.1007/s11009-011-9233-z
© Springer Science + Business Media, LLC 2011 The original publication is available at www.springerlink.com
数学
Springer US
http://hdl.handle.net/2241/117757