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Modelling Spatial Dependence in Mult...

Timmins, Tracy.

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Modelling Spatial Dependence in Multivariate Regression Models of Grizzly Bear Health in Alberta, Canada.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modelling Spatial Dependence in Multivariate Regression Models of Grizzly Bear Health in Alberta, Canada./
作者:	Timmins, Tracy.
面頁冊數:	171 p.
附註:	Source: Masters Abstracts International, Volume: 49-03, page: 1560.
Contained By:	Masters Abstracts International49-03.
標題:	Agriculture, Wildlife Management. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR69554
ISBN:	9780494695548

Modelling Spatial Dependence in Multivariate Regression Models of Grizzly Bear Health in Alberta, Canada.
Timmins, Tracy.

Modelling Spatial Dependence in Multivariate Regression Models of Grizzly Bear Health in Alberta, Canada. - 171 p.

Source: Masters Abstracts International, Volume: 49-03, page: 1560.

Thesis (M.Sc.)--University of Calgary (Canada), 2010.

To ensure the preservation of grizzly bears in Alberta, the effect of environmental change on grizzly bear health must be understood. This thesis investigated the relationship between grizzly bear health and environment using regression models with a focus on modelling spatial dependence. Ordinary least squares (OLS) regression models tend to misestimate the variance of estimated parameters in the presence of spatial dependence. This can lead to faulty inferences.

ISBN: 9780494695548Subjects--Topical Terms:

1670004
Agriculture, Wildlife Management.

Modelling Spatial Dependence in Multivariate Regression Models of Grizzly Bear Health in Alberta, Canada.
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520 $a To ensure the preservation of grizzly bears in Alberta, the effect of environmental change on grizzly bear health must be understood. This thesis investigated the relationship between grizzly bear health and environment using regression models with a focus on modelling spatial dependence. Ordinary least squares (OLS) regression models tend to misestimate the variance of estimated parameters in the presence of spatial dependence. This can lead to faulty inferences.
520 $a A variety of spatial neighbourhoods were created to detect spatial dependence in OLS regression models and to develop spatial autoregressive models as an alternative. It was found that significant negative spatial dependence was present in the grizzly bear data and caused the variance of parameters to be overestimated in OLS models. The use of spatial autoregressive models improved the fit of the models and increased the significance of the parameter estimates. Those spatial neighbourhoods that grouped together known grizzly bear population groups were best able to detect spatial dependence.
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