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Continuous field statistical methods...

Nagle, Nicholas.

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Continuous field statistical methods for spatial analysis in the social sciences.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Continuous field statistical methods for spatial analysis in the social sciences./
Author:	Nagle, Nicholas.
Description:	129 p.
Notes:	Chairs: Stuart Sweeney; Phaedon Kyriakidis.
Contained By:	Dissertation Abstracts International66-08A.
Subject:	Economics, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3186843
ISBN:	9780542281402

Continuous field statistical methods for spatial analysis in the social sciences.
Nagle, Nicholas.

Continuous field statistical methods for spatial analysis in the social sciences. - 129 p.

Chairs: Stuart Sweeney; Phaedon Kyriakidis.

Thesis (Ph.D.)--University of California, Santa Barbara, 2005.

Many standard references on spatial analysis separate techniques into continuous field techniques and discrete field, or "lattice-based" techniques. The continuous field techniques, which receive their most complete development within the field of geostatistics, are founded on the premise of covariance stationarity and the existence of a known model of spatial covariance. These assumptions have been regarded as implausible by many spatial analysts in the social sciences, particularly for the analysis of areal data. Thus, the continuous field methods have been largely dismissed as irrelevant for social science research.

ISBN: 9780542281402Subjects--Topical Terms:

1017424
Economics, General.

Continuous field statistical methods for spatial analysis in the social sciences.
LDR:02982nam 2200337 a 45 001 970522
005 20110921
008 110921s2005 eng d
020 $a 9780542281402
035 $a (UMI)AAI3186843
035 $a AAI3186843
040 $a UMI $c UMI
100 1 $a Nagle, Nicholas. $3 1294561
245 1 0 $a Continuous field statistical methods for spatial analysis in the social sciences.
300 $a 129 p.
500 $a Chairs: Stuart Sweeney; Phaedon Kyriakidis.
500 $a Source: Dissertation Abstracts International, Volume: 66-08, Section: A, page: 3048.
502 $a Thesis (Ph.D.)--University of California, Santa Barbara, 2005.
520 $a Many standard references on spatial analysis separate techniques into continuous field techniques and discrete field, or "lattice-based" techniques. The continuous field techniques, which receive their most complete development within the field of geostatistics, are founded on the premise of covariance stationarity and the existence of a known model of spatial covariance. These assumptions have been regarded as implausible by many spatial analysts in the social sciences, particularly for the analysis of areal data. Thus, the continuous field methods have been largely dismissed as irrelevant for social science research.
520 $a This dissertation presents three new continuous field techniques to help alleviate these concerns. First, a nonparametric estimator is presented that generates valid spatial covariance functions, thus relaxing the requirement of a known spatial covariance function. This estimator is simpler than existing nonparametric estimators and relies on derivative constraints that are well-known in the geostatistics literature.
520 $a Second, a method for specifying the covariance structure for areal data from a stationary, parametric covariance model is specified. It is shown that within some limitations it is possible to estimate the parameters of the covariance function when only areal data are present. Monte Carlo simulation results suggest that this covariance estimator provides correctly sized confidence intervals when used in a feasible generalized least squares procedure for the spatial linear regression model.
520 $a Third, a generalized method of moments (GMM) estimator is proposed for situations when both geographically aggregated areal data and aspatial microdata are available, as is the case for many census data. Monte Carlo evidence suggest that the GMM estimator effectively combines information from both data, providing correctly sized confidence intervals and improved precision over the areal data estimators.
590 $a School code: 0035.
650 4 $a Economics, General. $3 1017424
650 4 $a Sociology, Theory and Methods. $3 626625
650 4 $a Geography. $3 524010
690 $a 0344
690 $a 0366
690 $a 0501
710 2 0 $a University of California, Santa Barbara. $3 1017586
773 0 $t Dissertation Abstracts International $g 66-08A.
790 $a 0035
790 1 0 $a Kyriakidis, Phaedon, $e advisor
790 1 0 $a Sweeney, Stuart, $e advisor
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3186843