東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

High-dimensional Universal Dependenc...

Peng, Hesen.

FindBook

Google Book

Amazon

博客來

High-dimensional Universal Dependence Discovery.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	High-dimensional Universal Dependence Discovery./
作者:	Peng, Hesen.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2012,
面頁冊數:	86 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-01(E), Section: B.
Contained By:	Dissertation Abstracts International74-01B(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3522342
ISBN:	9781267544261

High-dimensional Universal Dependence Discovery.
Peng, Hesen.

High-dimensional Universal Dependence Discovery. - Ann Arbor : ProQuest Dissertations & Theses, 2012 - 86 p.

Source: Dissertation Abstracts International, Volume: 74-01(E), Section: B.

Thesis (Ph.D.)--Emory University, 2012.

This item is not available from ProQuest Dissertations & Theses.

The emergence of high-throughput data in biological science and computer networks has generated novel challenges for statistical methods. Nonlinear relationships and multivariate interactions are abundant. The sheer volume of high-throughput data has limited the application for traditional case-by-case analysis methods, whose model assumptions, like linearity, are often not supported in high-throughput scenarios.

ISBN: 9781267544261Subjects--Topical Terms:

517247
Statistics.

High-dimensional Universal Dependence Discovery.
LDR:02504nmm a2200325 4500 001 2054858
005 20170424144343.5
008 170519s2012 ||||||||||||||||| ||eng d
020 $a 9781267544261
035 $a (MiAaPQ)AAI3522342
035 $a AAI3522342
040 $a MiAaPQ $c MiAaPQ
100 1 $a Peng, Hesen. $3 3168434
245 1 0 $a High-dimensional Universal Dependence Discovery.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2012
300 $a 86 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-01(E), Section: B.
500 $a Adviser: Tianwei Yu.
502 $a Thesis (Ph.D.)--Emory University, 2012.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a The emergence of high-throughput data in biological science and computer networks has generated novel challenges for statistical methods. Nonlinear relationships and multivariate interactions are abundant. The sheer volume of high-throughput data has limited the application for traditional case-by-case analysis methods, whose model assumptions, like linearity, are often not supported in high-throughput scenarios.
520 $a To meet these challenges, we developed Mira score, a novel probabilistic association statistic that accounts for high-dimensional universal dependence. Mira score is defined as a function of observation graph, and thus circumvents the curse of dimensionality in high-dimensional data. The superior statistical property enjoyed by Mira score has led to our development of an efficient network reverse-engineering procedure for multivariate dependence. As an example, the procedure has been applied to celiac disease and lung cancer pathway interaction analysis, and has achieved interesting findings.
520 $a Further more, in the supervised-machine learning scenario, we proposed SeMira procedure, an efficient variable selection procedure that accounts for high-dimensional universal dependence. The SeMira procedure is capable of identifying universal probabilistic association between multivariate response variables and high-dimensional predictors. The highly desirable statistical property of the SeMira procedure is discussed and numerical study is conducted using both simulated and real genetic pathway data.
590 $a School code: 0665.
650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
690 $a 0463
690 $a 0308
710 2 $a Emory University. $b Biostatistics. $3 3168435
773 0 $t Dissertation Abstracts International $g 74-01B(E).
790 $a 0665
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3522342