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

Borrowing from Your Neighbors: Three Statistical Techniques from Nontraditional Sources.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Borrowing from Your Neighbors: Three Statistical Techniques from Nontraditional Sources./
作者:	Hoffman, Kentaro J.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	116 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29068109
ISBN:	9798802725917

Borrowing from Your Neighbors: Three Statistical Techniques from Nontraditional Sources.
Hoffman, Kentaro J.

Borrowing from Your Neighbors: Three Statistical Techniques from Nontraditional Sources. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 116 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2022.

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

From Generalised Fiducial Inference to Causal Inference, the past few years have seen a rising tide of new statistical paradigms calling into question our previous approaches of learning from data. This thesis will follow in this movement and demonstrate how these newer paradigms allow us to perform analyses that would be difficult to perform using conventional approaches. In the first chapter, we show how Dempster-Shafer and Fidu- cial Inference can be used as an alternative approach to the conventional Neyman-Pearson hypothesis testing paradigm through the inclusion of an "unknown" class into the testing procedure. This not only allows for tests with in-built robustness estimates, but allows for a natural analysis of the effects of adversarial attacks on hypothesis tests. In the second chap- ter, we demonstrate how interpretable causal inference combine with differential equation modeling gives users a powerful new approach to answering causal questions about patients exhibiting epileptiform activity. Finally, we combine the Empirical Mode Decomposition, which pioneered a signal decomposition that makes far fewer assumptions than traditional Fourier or Wavelet decompositions, with statistical techniques to allow for more accurate signal identification and cleaning.

ISBN: 9798802725917Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Causal inference

Borrowing from Your Neighbors: Three Statistical Techniques from Nontraditional Sources.
LDR:02485nmm a2200373 4500 001 2350397
005 20221020125753.5
008 241004s2022 ||||||||||||||||| ||eng d
020 $a 9798802725917
035 $a (MiAaPQ)AAI29068109
035 $a AAI29068109
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hoffman, Kentaro J. $3 3689877
245 1 0 $a Borrowing from Your Neighbors: Three Statistical Techniques from Nontraditional Sources.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 116 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Advisor: Zhang, Kai;Rudin, Cynthia.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a From Generalised Fiducial Inference to Causal Inference, the past few years have seen a rising tide of new statistical paradigms calling into question our previous approaches of learning from data. This thesis will follow in this movement and demonstrate how these newer paradigms allow us to perform analyses that would be difficult to perform using conventional approaches. In the first chapter, we show how Dempster-Shafer and Fidu- cial Inference can be used as an alternative approach to the conventional Neyman-Pearson hypothesis testing paradigm through the inclusion of an "unknown" class into the testing procedure. This not only allows for tests with in-built robustness estimates, but allows for a natural analysis of the effects of adversarial attacks on hypothesis tests. In the second chap- ter, we demonstrate how interpretable causal inference combine with differential equation modeling gives users a powerful new approach to answering causal questions about patients exhibiting epileptiform activity. Finally, we combine the Empirical Mode Decomposition, which pioneered a signal decomposition that makes far fewer assumptions than traditional Fourier or Wavelet decompositions, with statistical techniques to allow for more accurate signal identification and cleaning.
590 $a School code: 0153.
650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
650 4 $a Artificial intelligence. $3 516317
653 $a Causal inference
653 $a Dempster-Shafer
653 $a Fiducial
653 $a Seizures
653 $a Statistical inference
690 $a 0463
690 $a 0308
690 $a 0800
710 2 $a The University of North Carolina at Chapel Hill. $b Statistics and Operations Research. $3 3179567
773 0 $t Dissertations Abstracts International $g 83-12B.
790 $a 0153
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29068109