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Loss function approaches to predict ...

Zhang, Jian.

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Loss function approaches to predict a spatial quantile and its exceedance region.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Loss function approaches to predict a spatial quantile and its exceedance region./
作者:	Zhang, Jian.
面頁冊數:	144 p.
附註:	Advisers: Peter F. Craigmile; Noel Cressie.
Contained By:	Dissertation Abstracts International67-12B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3244798

Loss function approaches to predict a spatial quantile and its exceedance region.
Zhang, Jian.

Loss function approaches to predict a spatial quantile and its exceedance region. - 144 p.

Advisers: Peter F. Craigmile; Noel Cressie.

Thesis (Ph.D.)--The Ohio State University, 2007.

An important problem in spatial statistics is to predict a spatial quantile and its associated exceedance region. This has applications in environmental sciences, natural resources, and agriculture, since unusual events tend to have a strong impact on the environment. In this dissertation, we first review loss-function approaches to quantify exceedances. We then develop a method for the prediction of the spatial exceedance region involving a class of loss functions based on image metrics. We give special attention to Baddeley's loss function, for which we calibrate the choice of a tuning parameter. We then propose a joint-loss approach for the prediction of both a spatial quantile and its associated exceedance region. The optimal predictor is obtained by minimizing the posterior expected loss, given the spatial-trend, noise, and spatial-covariance parameters. In practice, the parameters are estimated and the minimization involves simulated annealing. We compare various predictors' performances through a simulation and apply our methodology to a spatial dataset of decadal temperature change over the Americas.Subjects--Topical Terms:

517247
Statistics.

Loss function approaches to predict a spatial quantile and its exceedance region.
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100 1 $a Zhang, Jian. $3 1057605
245 1 0 $a Loss function approaches to predict a spatial quantile and its exceedance region.
300 $a 144 p.
500 $a Advisers: Peter F. Craigmile; Noel Cressie.
500 $a Source: Dissertation Abstracts International, Volume: 67-12, Section: B, page: 7157.
502 $a Thesis (Ph.D.)--The Ohio State University, 2007.
520 $a An important problem in spatial statistics is to predict a spatial quantile and its associated exceedance region. This has applications in environmental sciences, natural resources, and agriculture, since unusual events tend to have a strong impact on the environment. In this dissertation, we first review loss-function approaches to quantify exceedances. We then develop a method for the prediction of the spatial exceedance region involving a class of loss functions based on image metrics. We give special attention to Baddeley's loss function, for which we calibrate the choice of a tuning parameter. We then propose a joint-loss approach for the prediction of both a spatial quantile and its associated exceedance region. The optimal predictor is obtained by minimizing the posterior expected loss, given the spatial-trend, noise, and spatial-covariance parameters. In practice, the parameters are estimated and the minimization involves simulated annealing. We compare various predictors' performances through a simulation and apply our methodology to a spatial dataset of decadal temperature change over the Americas.
590 $a School code: 0168.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 $a The Ohio State University. $3 718944
773 0 $t Dissertation Abstracts International $g 67-12B.
790 $a 0168
790 1 0 $a Craigmile, Peter F., $e advisor
790 1 0 $a Cressie, Noel, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3244798