Local influence analysis in general spatial models

Xiaowen Dai; Libin Jin; Lei Shi; Cuiping Yang; Shuangzhe LIU

doi:10.1007/s10182-015-0261-9

Local influence analysis in general spatial models

Xiaowen Dai, Libin Jin, Lei Shi, Cuiping Yang, Shuangzhe LIU

Research output: Contribution to journal › Article

2 Citations (Scopus)

Abstract

We study the local influence in the general spatial model which includes the spatial autoregressive model and the spatial error model as two special cases. The stepwise local influence procedure is employed in our diagnostic analysis. We derive the local diagnostic measures in the general spatial model under three perturbation schemes, namely, the variance perturbation, dependent variable perturbation and explanatory variable perturbation schemes. A simulation example and two realdata examples are analysed in detail and they show that the stepwise local influence analysis is effective in identifying influential observations and is a powerful tool for uncovering masking effects.

Original language	English
Pages (from-to)	313-331
Number of pages	19
Journal	AStA Advances in Statistical Analysis
Volume	100
Issue number	3
DOIs	https://doi.org/10.1007/s10182-015-0261-9
Publication status	Published - 2016

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Dai, X., Jin, L., Shi, L., Yang, C., & LIU, S. (2016). Local influence analysis in general spatial models. AStA Advances in Statistical Analysis, 100(3), 313-331. https://doi.org/10.1007/s10182-015-0261-9

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abstract = "We study the local influence in the general spatial model which includes the spatial autoregressive model and the spatial error model as two special cases. The stepwise local influence procedure is employed in our diagnostic analysis. We derive the local diagnostic measures in the general spatial model under three perturbation schemes, namely, the variance perturbation, dependent variable perturbation and explanatory variable perturbation schemes. A simulation example and two realdata examples are analysed in detail and they show that the stepwise local influence analysis is effective in identifying influential observations and is a powerful tool for uncovering masking effects.",

keywords = "general spatial model, influential observation, perturbation scheme, General spatial model, Stepwise local influence analysis, Perturbation scheme, Influential observation",

author = "Xiaowen Dai and Libin Jin and Lei Shi and Cuiping Yang and Shuangzhe LIU",

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language = "English",

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AU - Dai, Xiaowen

AU - Jin, Libin

AU - Shi, Lei

AU - Yang, Cuiping

AU - LIU, Shuangzhe

PY - 2016

Y1 - 2016

N2 - We study the local influence in the general spatial model which includes the spatial autoregressive model and the spatial error model as two special cases. The stepwise local influence procedure is employed in our diagnostic analysis. We derive the local diagnostic measures in the general spatial model under three perturbation schemes, namely, the variance perturbation, dependent variable perturbation and explanatory variable perturbation schemes. A simulation example and two realdata examples are analysed in detail and they show that the stepwise local influence analysis is effective in identifying influential observations and is a powerful tool for uncovering masking effects.

AB - We study the local influence in the general spatial model which includes the spatial autoregressive model and the spatial error model as two special cases. The stepwise local influence procedure is employed in our diagnostic analysis. We derive the local diagnostic measures in the general spatial model under three perturbation schemes, namely, the variance perturbation, dependent variable perturbation and explanatory variable perturbation schemes. A simulation example and two realdata examples are analysed in detail and they show that the stepwise local influence analysis is effective in identifying influential observations and is a powerful tool for uncovering masking effects.

KW - general spatial model

KW - influential observation

KW - perturbation scheme

KW - General spatial model

KW - Stepwise local influence analysis

KW - Perturbation scheme

KW - Influential observation

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UR - http://www.mendeley.com/research/local-influence-analysis-general-spatial-models

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DO - 10.1007/s10182-015-0261-9

M3 - Article

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EP - 331

JO - AStA Advances in Statistical Analysis

JF - AStA Advances in Statistical Analysis

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10.1007/s10182-015-0261-9