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  • Search: subject:"spatial basis functions"
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Year of publication
Subject
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Australian census 3 Moran operator 3 asymmetric spatial dependence matrix 3 heteroskedasticity 3 spatial autoregressive model 3 spatial basis functions 3 spatial random effects model 3 Autocorrelation 1 Autokorrelation 1 Regional economics 1 Regionalökonomik 1 Räumliche Interaktion 1 Spatial interaction 1 Theorie 1 Theory 1
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Online availability
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Free 3
Type of publication
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Article 3
Type of publication (narrower categories)
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Article 1 Article in journal 1 Aufsatz in Zeitschrift 1
Language
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English 2 Undetermined 1
Author
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Burden, Sandy 3 Steel, David G. 3 Cressie, Noel 2 Cressie, Noel A. C. 1
Published in...
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Econometrics 2 Econometrics : open access journal 1
Source
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ECONIS (ZBW) 1 EconStor 1 RePEc 1
Showing 1 - 3 of 3
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The SAR model for very large datasets: A reduced rank approach
Burden, Sandy; Cressie, Noel; Steel, David G. - In: Econometrics 3 (2015) 2, pp. 317-338
The SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-efficient spatial model is the spatial random...
Persistent link: https://www.econbiz.de/10011755283
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Cover Image
The SAR Model for Very Large Datasets: A Reduced Rank Approach
Burden, Sandy; Cressie, Noel; Steel, David G. - In: Econometrics 3 (2015) 2, pp. 317-338
The SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-efficient spatial model is the spatial random...
Persistent link: https://www.econbiz.de/10011276475
Saved in:
Cover Image
The SAR model for very large datasets : a reduced rank approach
Burden, Sandy; Cressie, Noel A. C.; Steel, David G. - In: Econometrics : open access journal 3 (2015) 2, pp. 317-338
The SAR model is widely used in spatial econometrics to model Gaussian processes on a discrete spatial lattice, but for large datasets, fitting it becomes computationally prohibitive, and hence, its usefulness can be limited. A computationally-efficient spatial model is the spatial random...
Persistent link: https://www.econbiz.de/10011297645
Saved in:
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