Efficient Estimation Strategies for Spatial Moving Average Model

  • Marwan Al-MomaniEmail author
  • Syed Ejaz Ahmed
  • Abdul A. Hussein
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1001)


Data collected over a geographical space may exhibit some sort of dependence in the sense that closer observations are more alike than those far apart. Such behavior is modeled by including a covariance structure into the classical statistical models. In particular, spatial regression models which accommodate various types of spatial dependencies have been increasingly applied in epidemiology, geology, disease surveillance, urban planning, analysis and mapping of poverty indicators and others. An important type of spatial regression models is the Spatial Moving Average (SMA, which imposes a moving average specification on the noise term, as is the case in temporal time series regressions. In this paper we consider the SMA models and propose efficient estimators of their regression coefficients by using shrinkage and penalty approaches. We provide analytical and numerical analysis to illustrate the superiority of the proposed estimators over the classical MLE estimators. Additionally, we apply the new methodology to the Baltimore housing sale prices data.


Spatial moving average Pre test Shrinkage Penalty estimators 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marwan Al-Momani
    • 1
    Email author
  • Syed Ejaz Ahmed
    • 2
  • Abdul A. Hussein
    • 3
  1. 1.Department of Mathematics and StatisticsKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia
  2. 2.Department of MathematicsBrock UniversitySt. CatharinesCanada
  3. 3.Department of Mathematics and StatisticsUniversity of WindsorWindsorCanada

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