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

Exploring latent structure in spatia...

Calder, Catherine Agnes.

Linked to FindBook

Google Book

Amazon

博客來

Exploring latent structure in spatial temporal processes using process convolutions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Exploring latent structure in spatial temporal processes using process convolutions./
Author:	Calder, Catherine Agnes.
Description:	98 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3887.
Contained By:	Dissertation Abstracts International64-08B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3102242
ISBN:	0496498126

Exploring latent structure in spatial temporal processes using process convolutions.
Calder, Catherine Agnes.

Exploring latent structure in spatial temporal processes using process convolutions. - 98 p.

Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3887.

Thesis (Ph.D.)--Duke University, 2003.

Bayesian process convolution models provide an appealing approach for modeling spatial temporal data. Their structure can be exploited to significantly reduce the dimensionality of a complex spatial temporal process. In addition, process convolution models can provide insight into the space-time dependence structure of the process.

ISBN: 0496498126Subjects--Topical Terms:

517247
Statistics.

Exploring latent structure in spatial temporal processes using process convolutions.
LDR:02870nmm 2200337 4500 001 1843862
005 20051017073408.5
008 130614s2003 eng d
020 $a 0496498126
035 $a (UnM)AAI3102242
035 $a AAI3102242
040 $a UnM $c UnM
100 1 $a Calder, Catherine Agnes. $3 1932077
245 1 0 $a Exploring latent structure in spatial temporal processes using process convolutions.
300 $a 98 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3887.
500 $a Supervisors: David Higdon; Michael Lavine.
502 $a Thesis (Ph.D.)--Duke University, 2003.
520 $a Bayesian process convolution models provide an appealing approach for modeling spatial temporal data. Their structure can be exploited to significantly reduce the dimensionality of a complex spatial temporal process. In addition, process convolution models can provide insight into the space-time dependence structure of the process.
520 $a We illustrate a univariate version of these dynamic process convolution models using daily ozone concentration readings from 512 weather stations in the Eastern United States. Our hope is to better understand the space-time dependence in ozone concentrations over this region. The computational advantages of the model are highlighted.
520 $a Dynamic process convolution models can easily be extended to model multivariate spatial time-series. Instead of specifying the cross-covariance structure directly, we construct an underlying dynamic factor model that provides insight into the co-variance structure. By constructing a factor model, we further reduce the model's dimension temporally. Each of the factors evolves over time, and the data are modeled as a smoothed weighted average of these underlying factor processes. Inference procedures remain computationally tractable due to the additional reduction in the dimensionality of the model. We illustrate this model using multivariate pollutant data from the EPA's Clean Air Status and Trends Network (CASTNet).
520 $a Finally, a multiresolution dynamic factor process convolution model is proposed to gain insight into the dynamics of spatial processes at different scales. In most applications, global temporal trends can be modeled at fairly coarse resolutions whereas purely spatial variation takes place at fine scales. The CASTNet data is again analyzed by adding an additional process convolution field constructed from a fine-scale, temporally independent underlying process.
590 $a School code: 0066.
650 4 $a Statistics. $3 517247
650 4 $a Environmental Sciences. $3 676987
650 4 $a Physics, Atmospheric Science. $3 1019431
690 $a 0463
690 $a 0768
690 $a 0608
710 2 0 $a Duke University. $3 569686
773 0 $t Dissertation Abstracts International $g 64-08B.
790 1 0 $a Higdon, David, $e advisor
790 1 0 $a Lavine, Michael, $e advisor
790 $a 0066
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3102242