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Methods for resource selection studi...

El-Houbi, Ashraf.

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Methods for resource selection studies using correlated data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Methods for resource selection studies using correlated data./
作者:	El-Houbi, Ashraf.
面頁冊數:	157 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3525.
Contained By:	Dissertation Abstracts International65-07B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3139898
ISBN:	0496870396

Methods for resource selection studies using correlated data.
El-Houbi, Ashraf.

Methods for resource selection studies using correlated data. - 157 p.

Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3525.

Thesis (Ph.D.)--University of Wyoming, 2004.

The estimation of resource selection functions by animals has received a great deal of attention recently. The focus of the application and motivation of the research are on habitat choices made by wildlife. There are several statistical modeling techniques for resource selection. Common models in the resource selection literature, such as logistic regression, assume units are sampled randomly and independently. When individual animals relocate over time, it is assumed that relocations are independent of one another; that is they are not spatially or temporally correlated. This assumption may be violated, when, for example, relocations are close to one another over an interval of time.

ISBN: 0496870396Subjects--Topical Terms:

517247
Statistics.

Methods for resource selection studies using correlated data.
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502 $a Thesis (Ph.D.)--University of Wyoming, 2004.
520 $a The estimation of resource selection functions by animals has received a great deal of attention recently. The focus of the application and motivation of the research are on habitat choices made by wildlife. There are several statistical modeling techniques for resource selection. Common models in the resource selection literature, such as logistic regression, assume units are sampled randomly and independently. When individual animals relocate over time, it is assumed that relocations are independent of one another; that is they are not spatially or temporally correlated. This assumption may be violated, when, for example, relocations are close to one another over an interval of time.
520 $a The effect of correlation on inferences for resource selection has not been well studied. Most of current research ignores the problem and assumes independence from animal to animal. With recent technology such as Global Positioning Systems (GPS), more and more data violate these assumptions. In this thesis, I present techniques, like the variogram and the generalized linear mixed model (GLMM) approach, to model correlation problems and to improve the ways in which biologists model these kinds of animal behavior. GLMMs have a wide range of applications and practical importance. GLMMs can accommodate correlated and non-normally distributed data. They can accommodate a wide range of discrete, continuous and mixed data. By modeling the correlation problem correctly, we can achieve efficient estimates and accurate inferences, and we can perform the usual tests correctly. The elk data are used as a motivating example to develop the statistical methodology to model the correlation problem.
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