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

FindBook

Google Book

Amazon

博客來

Some Contributions to High-Dimensional Statistical Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Some Contributions to High-Dimensional Statistical Machine Learning./
作者:	Wei, Zhenyu.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	98 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=28542149
ISBN:	9798538100644

Some Contributions to High-Dimensional Statistical Machine Learning.
Wei, Zhenyu.

Some Contributions to High-Dimensional Statistical Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 98 p.

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

Thesis (Ph.D.)--University of California, Davis, 2021.

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

This dissertation makes contributions to the broad area of high-dimensional statistical machine learning. When the number of features p is much larger than the number of observations n, often written as p >> n, the problems are known as high-dimensional problems. With the rapid growth of the dimensionality and complexity of the data, such problems have become increasingly common and important, for example, in genomics and computational biology. This dissertation focuses on two such high-dimensional problems and develops solutions for them. The first problem concerns uncertainty quantification for the multi-task learning problem. Using the generalized fiducial inference framework, a novel method termed GMTask is developed. This method is shown to enjoy desirable theoretical and empirical properties. The second problem studies variable selection, robust estimation, and nonparametric additive model fitting in the high-dimensional scenario. The minimum description length principle is employed as one unified approach to simultaneous solve these issues.

ISBN: 9798538100644Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Generalized fiducial inference

Some Contributions to High-Dimensional Statistical Machine Learning.
LDR:02332nmm a2200397 4500 001 2352137
005 20221118093820.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798538100644
035 $a (MiAaPQ)AAI28542149
035 $a AAI28542149
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wei, Zhenyu. $3 3691759
245 1 0 $a Some Contributions to High-Dimensional Statistical Machine Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 98 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Lee, Thomas C.M.
502 $a Thesis (Ph.D.)--University of California, Davis, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation makes contributions to the broad area of high-dimensional statistical machine learning. When the number of features p is much larger than the number of observations n, often written as p >> n, the problems are known as high-dimensional problems. With the rapid growth of the dimensionality and complexity of the data, such problems have become increasingly common and important, for example, in genomics and computational biology. This dissertation focuses on two such high-dimensional problems and develops solutions for them. The first problem concerns uncertainty quantification for the multi-task learning problem. Using the generalized fiducial inference framework, a novel method termed GMTask is developed. This method is shown to enjoy desirable theoretical and empirical properties. The second problem studies variable selection, robust estimation, and nonparametric additive model fitting in the high-dimensional scenario. The minimum description length principle is employed as one unified approach to simultaneous solve these issues.
590 $a School code: 0029.
650 4 $a Statistics. $3 517247
650 4 $a Sparsity. $3 3680690
650 4 $a Simulation. $3 644748
650 4 $a Datasets. $3 3541416
650 4 $a Regression analysis. $3 529831
650 4 $a Experiments. $3 525909
650 4 $a Normal distribution. $3 3561025
650 4 $a Signal processing. $3 533904
650 4 $a Dissertations & theses. $3 3560115
650 4 $a Feature selection. $3 3560270
650 4 $a Noise. $3 598816
650 4 $a Methods. $3 3560391
650 4 $a Algorithms. $3 536374
650 4 $a Performance evaluation. $3 3562292
650 4 $a Computer science. $3 523869
650 4 $a Artificial intelligence. $3 516317
650 4 $a Information science. $3 554358
653 $a Generalized fiducial inference
653 $a High dimensional statistics
653 $a Minimum description length
653 $a Multi-task learning
653 $a Statistical learning
653 $a Uncertainty quantification
690 $a 0463
690 $a 0984
690 $a 0723
690 $a 0800
710 2 $a University of California, Davis. $b Statistics. $3 3191587
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0029
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28542149