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Machine Learning Methods in HDLSS Settings.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Methods in HDLSS Settings./
作者:	Yang, Xi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	99 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28649794
ISBN:	9798538118250

Machine Learning Methods in HDLSS Settings.
Yang, Xi.

Machine Learning Methods in HDLSS Settings. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 99 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2021.

This item must not be sold to any third party vendors.

During the exploration of high dimension-low-sample-size (HDLSS) data in different fields such as genetics, finance, computer science, etc, various machine learning methods have been developed. This dissertation includes the invention of novel methods and the improvement of current methods, which are evaluated using cancer genetics data.The statistical significance of the difference between subgroups is a central question in the setting of HDLSS data. The Direction Projection Permutation (DiProPerm) hypothesis test provides an answer to this that is directly connected to a visual analysis of the data. However, under some circumstances, the DiProPerm test can be less powerful and accurate when measuring the significance of the test pairs. In this dissertation, we first introduce a new permutation method. This increases the power of the test in high signal situations. Furthermore, the simulated null test statistics tend to be more reasonable and uni-modal. Then, our theoretical analysis provides an adjustment to the inference for both permutation schemes. This enables us to exploit the improved power available. We also add confidence measures that reflect the Monte Carlo uncertainty in that test, which is seen to be very useful for the comparison of results across different contexts.Another important goal of this dissertation is to understand the drivers of Angle-Based Joint and Individual Variation Explained (AJIVE). An important open problem is a statistical inference on the AJIVE loadings to determine which are significant features of the analysis. Jackstraw is a method that generally aims to find the statistically significant drivers associated with unobserved latent variables. In this dissertation, we develop a method based on similar ideas in the richer context of AJIVE to give a precise estimation.Genetic data sets are used to evaluate the above-proposed machine learning methods, which also give results of independent interest to biologists.

ISBN: 9798538118250Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Machine Learning

Machine Learning Methods in HDLSS Settings.
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