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

Resampling Methods in Statistical Inference.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Resampling Methods in Statistical Inference./
作者:	Zhou, Weilian.
面頁冊數:	1 online resource (86 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
Contained By:	Dissertations Abstracts International84-09B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30308537click for full text (PQDT)
ISBN:	9798374475975

Resampling Methods in Statistical Inference.
Zhou, Weilian.

Resampling Methods in Statistical Inference. - 1 online resource (86 pages)

Source: Dissertations Abstracts International, Volume: 84-09, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2021.

Includes bibliographical references

In statistical inference, statisticians are trying to infer the property of the population from the samples. In most cases, we are not only interested in the point estimation but also the uncertainty of the estimation such as bias and variance. However, it is impossible to derive the explicit formula of the interest quantity. Resampling methods, a class of nonparametric methods, play an important role in estimating the interested quantities without approaching the explicit formula. There are two main kinds of resampling methods, the Bootstrap and the Jackknife. For Bootstrap, it estimates the sample distribution by sampling with replacement from the original data. For any statistics with the form of the functional of the population distribution, it is very natural to utilize the bootstrap method to construct a plug-in estimator by replacing the population distribution by the empirical distribution. Unlike the bootstrap method, the Jackknife method computes the parameter by systematically leaving out samples. It is useful in estimating the bias and the variance of the statistics. Since the jackknife method doesn't approximate the population distribution, it is not applied as generally as the bootstrap method. However, the jackknife method enjoys the advantage of efficient computing. It is useful in estimating the bias and the variance of the estimators. In this dissertation, we explore the resampling methods in different situations.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798374475975Subjects--Topical Terms:

517247
Statistics.
Index Terms--Genre/Form:

542853
Electronic books.

Resampling Methods in Statistical Inference.
LDR:02660nmm a2200325K 4500 001 2359953
005 20230925131718.5
006 m o d
007 cr mn ---uuuuu
008 241011s2021 xx obm 000 0 eng d
020 $a 9798374475975
035 $a (MiAaPQ)AAI30308537
035 $a (MiAaPQ)NCState_Univ18402040213
035 $a AAI30308537
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Zhou, Weilian. $3 3700570
245 1 0 $a Resampling Methods in Statistical Inference.
264 0 $c 2021
300 $a 1 online resource (86 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
500 $a Advisor: Lahiri, Soumendra; Chi, Eric.
502 $a Thesis (Ph.D.)--North Carolina State University, 2021.
504 $a Includes bibliographical references
520 $a In statistical inference, statisticians are trying to infer the property of the population from the samples. In most cases, we are not only interested in the point estimation but also the uncertainty of the estimation such as bias and variance. However, it is impossible to derive the explicit formula of the interest quantity. Resampling methods, a class of nonparametric methods, play an important role in estimating the interested quantities without approaching the explicit formula. There are two main kinds of resampling methods, the Bootstrap and the Jackknife. For Bootstrap, it estimates the sample distribution by sampling with replacement from the original data. For any statistics with the form of the functional of the population distribution, it is very natural to utilize the bootstrap method to construct a plug-in estimator by replacing the population distribution by the empirical distribution. Unlike the bootstrap method, the Jackknife method computes the parameter by systematically leaving out samples. It is useful in estimating the bias and the variance of the statistics. Since the jackknife method doesn't approximate the population distribution, it is not applied as generally as the bootstrap method. However, the jackknife method enjoys the advantage of efficient computing. It is useful in estimating the bias and the variance of the estimators. In this dissertation, we explore the resampling methods in different situations.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 517247
650 4 $a Statistical inference. $3 3700481
650 4 $a Time series. $3 3561811
650 4 $a Sample variance. $3 3680758
650 4 $a Parameter estimation. $3 567557
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a North Carolina State University. $3 1018772
773 0 $t Dissertations Abstracts International $g 84-09B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30308537 $z click for full text (PQDT)