The application of spatial Cliff–Ord models requires information about spatial coordinates of statistical units to be reliable, which is usually the case in the context of areal data. With micro-geographic point-level data, however, such information is inevitably affected by locational errors, that can be generated intentionally by the data producer for privacy protection or can be due to inaccuracy of the geocoding procedures. This unfortunate circumstance can potentially limit the use of the spatial autoregressive modelling framework for the analysis of micro data, as the presence of locational errors may have a non-negligible impact on the estimates of model parameters. This contribution aims at developing a strategy to reduce the bias and produce more reliable inference for spatial models with location errors. The proposed estimation strategy models both the spatial stochastic process and the coarsening mechanism by means of a marked point process. The model is fitted through the maximisation of a doubly-marginalised likelihood function of the marked point process, which cleans out the effects of coarsening. The validity of the proposed approach is assessed by means of a Monte Carlo simulation study under different real-case scenarios, whereas it is applied to real data on house prices.

Santi, F., Dickson, M. M., Giuliani, D., Arbia, G., Espa, G., (Abstract) Reduced-bias estimation of spatial autoregressive models with incompletely geocoded data, <<COMPUTATIONAL STATISTICS>>, 2021; 2021 (N/A): N/A-N/A/A. [doi:10.1007/s00180-021-01090-7] [http://hdl.handle.net/10807/177099]

Reduced-bias estimation of spatial autoregressive models with incompletely geocoded data

Arbia, G.
Ultimo
;
2021

Abstract

The application of spatial Cliff–Ord models requires information about spatial coordinates of statistical units to be reliable, which is usually the case in the context of areal data. With micro-geographic point-level data, however, such information is inevitably affected by locational errors, that can be generated intentionally by the data producer for privacy protection or can be due to inaccuracy of the geocoding procedures. This unfortunate circumstance can potentially limit the use of the spatial autoregressive modelling framework for the analysis of micro data, as the presence of locational errors may have a non-negligible impact on the estimates of model parameters. This contribution aims at developing a strategy to reduce the bias and produce more reliable inference for spatial models with location errors. The proposed estimation strategy models both the spatial stochastic process and the coarsening mechanism by means of a marked point process. The model is fitted through the maximisation of a doubly-marginalised likelihood function of the marked point process, which cleans out the effects of coarsening. The validity of the proposed approach is assessed by means of a Monte Carlo simulation study under different real-case scenarios, whereas it is applied to real data on house prices.
Inglese
Santi, F., Dickson, M. M., Giuliani, D., Arbia, G., Espa, G., (Abstract) Reduced-bias estimation of spatial autoregressive models with incompletely geocoded data, <<COMPUTATIONAL STATISTICS>>, 2021; 2021 (N/A): N/A-N/A/A. [doi:10.1007/s00180-021-01090-7] [http://hdl.handle.net/10807/177099]
File in questo prodotto:
File Dimensione Formato  
Reduced-bias estimation of spatial autoregressive models with incompletely geocoded data.pdf

accesso aperto

Tipologia file ?: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 528.54 kB
Formato Adobe PDF
528.54 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10807/177099
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact