Inner View of Spatial Autoregression Residuals with LADS and Join-Count Statistics

João Garrott Marques Negreiros; Marco Octávio Trindade Painho

Inner View of Spatial Autoregression Residuals with LADS and Join-Count Statistics

João Garrott Marques Negreiros, Marco Octávio Trindade Painho

NOVA Information Management School (NOVA IMS)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Spatial autocorrelation is a reality but also a requirement to carry out spatial interpolation and spatial autoregressive modeling. Conventional statistics must, then, be reformulated to properly account for spatial correlation and spatial heterogeneity within georeferenced data. For instance, if autoregressive residuals reveal a medium-strong spatial autocorrelation then any omitted variable, within the initial regression model, can be significant. Hence, LADS and join-count statistics are reviewed here as a way to test this missing explanatory variables through its spatial binary residuals because spatial autocorrelation error can be defined as a specification error artifact for spatial modeling.

Original language	Unknown
Title of host publication	Conference of the International Statistic Institute
Pages	1-5
Publication status	Published - 1 Jan 2009
Event	57th Conference of the International Statistic Institute - Duration: 1 Jan 2009 → …

Conference

Conference	57th Conference of the International Statistic Institute
Period	1/01/09 → …

Cite this

@inproceedings{2e53a659f9e942619f14adf3a3cde434,

title = "Inner View of Spatial Autoregression Residuals with LADS and Join-Count Statistics",

abstract = "Spatial autocorrelation is a reality but also a requirement to carry out spatial interpolation and spatial autoregressive modeling. Conventional statistics must, then, be reformulated to properly account for spatial correlation and spatial heterogeneity within georeferenced data. For instance, if autoregressive residuals reveal a medium-strong spatial autocorrelation then any omitted variable, within the initial regression model, can be significant. Hence, LADS and join-count statistics are reviewed here as a way to test this missing explanatory variables through its spatial binary residuals because spatial autocorrelation error can be defined as a specification error artifact for spatial modeling.",

keywords = "LADS, Spatial analysis, Spatial autoregression, Join-Count., Residuals",

author = "Negreiros, {Jo{\~a}o Garrott Marques} and Painho, {Marco Oct{\'a}vio Trindade}",

year = "2009",

month = jan,

day = "1",

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booktitle = "Conference of the International Statistic Institute",

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AU - Negreiros, João Garrott Marques

AU - Painho, Marco Octávio Trindade

PY - 2009/1/1

Y1 - 2009/1/1

N2 - Spatial autocorrelation is a reality but also a requirement to carry out spatial interpolation and spatial autoregressive modeling. Conventional statistics must, then, be reformulated to properly account for spatial correlation and spatial heterogeneity within georeferenced data. For instance, if autoregressive residuals reveal a medium-strong spatial autocorrelation then any omitted variable, within the initial regression model, can be significant. Hence, LADS and join-count statistics are reviewed here as a way to test this missing explanatory variables through its spatial binary residuals because spatial autocorrelation error can be defined as a specification error artifact for spatial modeling.

AB - Spatial autocorrelation is a reality but also a requirement to carry out spatial interpolation and spatial autoregressive modeling. Conventional statistics must, then, be reformulated to properly account for spatial correlation and spatial heterogeneity within georeferenced data. For instance, if autoregressive residuals reveal a medium-strong spatial autocorrelation then any omitted variable, within the initial regression model, can be significant. Hence, LADS and join-count statistics are reviewed here as a way to test this missing explanatory variables through its spatial binary residuals because spatial autocorrelation error can be defined as a specification error artifact for spatial modeling.

KW - LADS

KW - Spatial analysis

KW - Spatial autoregression

KW - Join-Count.

KW - Residuals

M3 - Conference contribution

SP - 1

EP - 5

BT - Conference of the International Statistic Institute

T2 - 57th Conference of the International Statistic Institute

Y2 - 1 January 2009

ER -