Please use this identifier to cite or link to this item:
Title: Multilevel modeling using spatial processes: Application to the Singapore housing market
Authors: Gelfand, A.E.
Banerjee, S.
Sirmans, C.F.
Tu, Y. 
Eng Ong, S.
Keywords: Kernel convolution
Multiplicative interactions
Multivariate spatial processes
Spatially varying coefficients
Issue Date: 2007
Citation: Gelfand, A.E., Banerjee, S., Sirmans, C.F., Tu, Y., Eng Ong, S. (2007). Multilevel modeling using spatial processes: Application to the Singapore housing market. Computational Statistics and Data Analysis 51 (7) : 3567-3579. ScholarBank@NUS Repository.
Abstract: Customary spatial modeling with point-referenced data introduces a modeling specification that includes a mean term, a spatial error or random effects term and a pure error term. The spatial random effects are usually modeled through a mean zero spatial process. If the mean term includes an intercept, then the spatial random effects can be interpreted as local spatial adjustments to the intercept. If the mean term is a familiar linear regression then it makes sense to ask whether the regression coefficients are constant or whether they might vary spatially, analogous to the intercept. This has been previously considered and the benefits of the increased flexibility have been demonstrated. The situation with replicates available at spatial locations is considered. This enables the building of the spatial analog of a multilevel model-replicate level covariates to explain the replicate level responses and location level covariates to explain the location level coefficients. The particular motivation for this modeling effort is a data set on condominium sales in Singapore. In this case, the replicates are the sales of condominiums within a building. Unit level features are available to explain the selling price of the unit and building level attributes to explain the coefficients. Anticipating dependence between coefficients, a multivariate spatial process specification is provided. This process is specified through kernel convolutions due to the computational challenges associated with fitting such models to a fairly large data set. There is flexibility in this kernel modeling necessitating model comparison. In particular, roughly 68,000 transactions across 1374 buildings (locations) are analyzed and the results and interpretation for the selected model are presented. © 2006 Elsevier B.V. All rights reserved.
Source Title: Computational Statistics and Data Analysis
ISSN: 01679473
DOI: 10.1016/j.csda.2006.11.019
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jul 31, 2020


checked on Jul 31, 2020

Page view(s)

checked on Aug 3, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.