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

Quantifying the Non-Linear Coupling ...

Esquerra-Ortells, Lledo.

FindBook

Google Book

Amazon

博客來

Quantifying the Non-Linear Coupling Between Species: Copula and Rank Based Statistics.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Quantifying the Non-Linear Coupling Between Species: Copula and Rank Based Statistics./
作者:	Esquerra-Ortells, Lledo.
面頁冊數:	56 p.
附註:	Source: Masters Abstracts International, Volume: 51-04.
Contained By:	Masters Abstracts International51-04(E).
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1533394
ISBN:	9781267912183

Quantifying the Non-Linear Coupling Between Species: Copula and Rank Based Statistics.
Esquerra-Ortells, Lledo.

Quantifying the Non-Linear Coupling Between Species: Copula and Rank Based Statistics. - 56 p.

Source: Masters Abstracts International, Volume: 51-04.

Thesis (M.S.)--University of Colorado at Boulder, 2012.

Understanding the process of coevolution, the evolution of interacting species, is a major endeavor of evolutionary biology. Coevolution is a potent source of adaptive evolution, since it can sustain selection pressures indefinitely even in the absence of changes in the physical environment. In this work, we want to explain eco-evolutionary patterns in terms of adaptive behavior. Pursuant to this goal our methodology and the accompanying metrics are aimed at capturing different aspects of the dependence that has been shaped by evolution to respond in dynamic, adaptive ways to relevant features of the organisms environment. Previous studies, Nuismer et al. 2010[14], have shown that adaptive behavior does not fit in a linear model. Consequently, we must go beyond correlation analysis to track the population dynamics and the evolutionary changes over time.

ISBN: 9781267912183Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Quantifying the Non-Linear Coupling Between Species: Copula and Rank Based Statistics.
LDR:02987nam a2200289 4500 001 1965323
005 20141022133319.5
008 150210s2012 ||||||||||||||||| ||eng d
020 $a 9781267912183
035 $a (MiAaPQ)AAI1533394
035 $a AAI1533394
040 $a MiAaPQ $c MiAaPQ
100 1 $a Esquerra-Ortells, Lledo. $3 2101966
245 1 0 $a Quantifying the Non-Linear Coupling Between Species: Copula and Rank Based Statistics.
300 $a 56 p.
500 $a Source: Masters Abstracts International, Volume: 51-04.
500 $a Adviser: Francois G. Meyer.
502 $a Thesis (M.S.)--University of Colorado at Boulder, 2012.
520 $a Understanding the process of coevolution, the evolution of interacting species, is a major endeavor of evolutionary biology. Coevolution is a potent source of adaptive evolution, since it can sustain selection pressures indefinitely even in the absence of changes in the physical environment. In this work, we want to explain eco-evolutionary patterns in terms of adaptive behavior. Pursuant to this goal our methodology and the accompanying metrics are aimed at capturing different aspects of the dependence that has been shaped by evolution to respond in dynamic, adaptive ways to relevant features of the organisms environment. Previous studies, Nuismer et al. 2010[14], have shown that adaptive behavior does not fit in a linear model. Consequently, we must go beyond correlation analysis to track the population dynamics and the evolutionary changes over time.
520 $a A variety of ways to measure dependence exist. We are interested in those which are capable of tracking non-linear dependence, since a vast amount of literature has been written about linear correlation meanwhile dealing with non-linear dependence seems almost an uncharted field. We studied different concepts that would allow us to find the coupling, if it exists, among the different species statistics. Among them, copula and rank based statistics turned out to be very promising. Hence, this work describes this copula concept, and how it is related to other rank statistic tools, giving some toy examples to explain how it helps in exploring the underlying structure of the data. Finally, we apply those concepts to analyze the dependence structure among the species of a simulated tritrophic system (predators- prey-resources or parasite-host-resources). We use the simulated data to explore two general questions. First, does coupling exist? And in the affirmative case, how is this dependence described? Second, does the underlying dependence structure of the predator-prey model describe the dependence between parasites and host; in other words, would we expect the two models to behave similarly in terms of adaptive behavior?.
590 $a School code: 0051.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Biology, General. $3 1018625
690 $a 0544
690 $a 0306
710 2 $a University of Colorado at Boulder. $b Electrical Engineering. $3 1025672
773 0 $t Masters Abstracts International $g 51-04(E).
790 $a 0051
791 $a M.S.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1533394