東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Bootstrap resampling in wavelet anal...

Yuan, Jiacheng.

Linked to FindBook

Google Book

Amazon

博客來

Bootstrap resampling in wavelet analysis and statistical methodologies in ecological research.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bootstrap resampling in wavelet analysis and statistical methodologies in ecological research./
Author:	Yuan, Jiacheng.
Description:	116 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-12, Section: B, page: 6711.
Contained By:	Dissertation Abstracts International66-12B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3203090
ISBN:	9780542484087

Bootstrap resampling in wavelet analysis and statistical methodologies in ecological research.
Yuan, Jiacheng.

Bootstrap resampling in wavelet analysis and statistical methodologies in ecological research. - 116 p.

Source: Dissertation Abstracts International, Volume: 66-12, Section: B, page: 6711.

Thesis (Ph.D.)--University of California, Santa Barbara, 2005.

Part I. Bootstrap resampling in wavelet analysis . Wavelet smoothing is often used to estimate an unknown signal function from observations of "function + noise" at n equi-spaced times. When n is very large, a big proportion of the discrete wavelet transform (DWT) coefficients are often very small compared to the others. In this scenario, it is reasonable to replace most small empirical DWT coefficients with zeros and leave the rest intact for the estimation of the DWT coefficients, which forms a variable selection problem. To implement variable selection, estimation of the noise standard deviation sigma is inevitable. The conventional estimator of sigma is the adjusted median absolute deviation of the finest empirical DWT coefficients, which is useful mostly for low frequency signals. To account for all types of signals, especially for high frequency signals, we developed the moving block estimator. A very popular variable selection method in literature is thresholding, and the common threshold level is 2logn , which is mostly based on the low frequency signals. We found a better threshold level, 2logn , based on all types of signals as well as the resampling behavior. We also developed another variable selection method: the optimal choice method, based on an estimate of expected loss. Beyond the point estimator of the DWT coefficients with variable selection, confidence sets based on two approximately pivotal quantities are constructed. Beran's key idea of shrink bootstrap is used in constructing the confidence sets.

ISBN: 9780542484087Subjects--Topical Terms:

517247
Statistics.

Bootstrap resampling in wavelet analysis and statistical methodologies in ecological research.
LDR:03132nmm 2200301 4500 001 1822739
005 20061129144940.5
008 130610s2005 eng d
020 $a 9780542484087
035 $a (UnM)AAI3203090
035 $a AAI3203090
040 $a UnM $c UnM
100 1 $a Yuan, Jiacheng. $3 1911872
245 1 0 $a Bootstrap resampling in wavelet analysis and statistical methodologies in ecological research.
300 $a 116 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-12, Section: B, page: 6711.
500 $a Adviser: David Hinkley.
502 $a Thesis (Ph.D.)--University of California, Santa Barbara, 2005.
520 $a Part I. Bootstrap resampling in wavelet analysis . Wavelet smoothing is often used to estimate an unknown signal function from observations of "function + noise" at n equi-spaced times. When n is very large, a big proportion of the discrete wavelet transform (DWT) coefficients are often very small compared to the others. In this scenario, it is reasonable to replace most small empirical DWT coefficients with zeros and leave the rest intact for the estimation of the DWT coefficients, which forms a variable selection problem. To implement variable selection, estimation of the noise standard deviation sigma is inevitable. The conventional estimator of sigma is the adjusted median absolute deviation of the finest empirical DWT coefficients, which is useful mostly for low frequency signals. To account for all types of signals, especially for high frequency signals, we developed the moving block estimator. A very popular variable selection method in literature is thresholding, and the common threshold level is 2logn , which is mostly based on the low frequency signals. We found a better threshold level, 2logn , based on all types of signals as well as the resampling behavior. We also developed another variable selection method: the optimal choice method, based on an estimate of expected loss. Beyond the point estimator of the DWT coefficients with variable selection, confidence sets based on two approximately pivotal quantities are constructed. Beran's key idea of shrink bootstrap is used in constructing the confidence sets.
520 $a Part II. Statistical methodologies in ecological research. Many statistical methodologies have been used by the author in his research work with ecological data. In this dissertation, three types of methodologies are discussed: (1) explorative (ordination) methodologies, including correspondence analysis and nonmetric scaling, (2) inferential (regression) methods, including analysis of covariance, mixed-effect models and partial least squares regression, (3) hybrid (combination of ordination and regression) methodologies, including redundancy analysis, canonical correspondence analysis and co-correspondence analysis.
590 $a School code: 0035.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Ecology. $3 1017726
650 4 $a Biology, Biostatistics. $3 1018416
690 $a 0463
690 $a 0329
690 $a 0308
710 2 0 $a University of California, Santa Barbara. $3 1017586
773 0 $t Dissertation Abstracts International $g 66-12B.
790 1 0 $a Hinkley, David, $e advisor
790 $a 0035
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3203090