A semiparametric spatial dynamic model
A semiparametric spatial dynamic model
Stimulated by the Boston house price data, in this paper, we propose a semiparametric spatial dynamic model, which extends the ordinary spatial autoregressive models to accommodate the effects of some covariates associated with the house price. A profile likelihood based estimation procedure is proposed. The asymptotic normality of the proposed estimators are derived. We also investigate how to identify the parametric/nonparametric components in the proposed semiparametric model. We show how many unknown parameters an unknown bivariate function amounts to, and propose an AIC/BIC of nonparametric version for model selection. Simulation studies are conducted to examine the performance of the proposed methods. The simulation results show our methods work very well. We finally apply the proposed methods to analyze the Boston house price data, which leads to some interesting findings
700-727
Sun, Yan
ef17cf27-e9bf-4dc3-940e-0e58903290b4
Yan, Hongjia
acc1e44e-b834-4a21-b05c-574b350f82b5
Zhang, Wenyang
0bf907b9-63ff-4ba2-8c90-20633a0681d3
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
2014
Sun, Yan
ef17cf27-e9bf-4dc3-940e-0e58903290b4
Yan, Hongjia
acc1e44e-b834-4a21-b05c-574b350f82b5
Zhang, Wenyang
0bf907b9-63ff-4ba2-8c90-20633a0681d3
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Sun, Yan, Yan, Hongjia, Zhang, Wenyang and Lu, Zudi
(2014)
A semiparametric spatial dynamic model.
The Annals of Statistics, 42 (2), .
(doi:10.1214/13-AOS1201).
Abstract
Stimulated by the Boston house price data, in this paper, we propose a semiparametric spatial dynamic model, which extends the ordinary spatial autoregressive models to accommodate the effects of some covariates associated with the house price. A profile likelihood based estimation procedure is proposed. The asymptotic normality of the proposed estimators are derived. We also investigate how to identify the parametric/nonparametric components in the proposed semiparametric model. We show how many unknown parameters an unknown bivariate function amounts to, and propose an AIC/BIC of nonparametric version for model selection. Simulation studies are conducted to examine the performance of the proposed methods. The simulation results show our methods work very well. We finally apply the proposed methods to analyze the Boston house price data, which leads to some interesting findings
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Published date: 2014
Organisations:
Statistical Sciences Research Institute
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Local EPrints ID: 370170
URI: http://eprints.soton.ac.uk/id/eprint/370170
ISSN: 0090-5364
PURE UUID: af98b3c5-980b-4101-aea0-2f701ebf624c
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Date deposited: 05 Oct 2015 07:51
Last modified: 15 Mar 2024 03:49
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Author:
Yan Sun
Author:
Hongjia Yan
Author:
Wenyang Zhang
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