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Variable Selection in Multivariate Linear Regression Model for Spatially Dependent Data

Jean Roland Ebende Penda et al.

Mathematical Methods of Statistics2025https://doi.org/10.3103/s1066530725700012article
ABDC B
Weight
0.37

What the paper says

This paper deals with variable selection in multivariate linear regression model when the data are observations on a spatial domain being a grid of sites in $$\mathbb{Z}^{d}$$ with $$d\geqslant 1$$ . We use a criterion that allows to characterize the subset of relevant variables as depending on two parameters, and we propose estimators for these parameters based on spatially dependent observations. We prove the consistency, under specified assumptions, of the method thus proposed. A simulation study made in order to assess the finite-sample behaviour of the proposed method with comparison to existing ones is presented.

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https://doi.org/https://doi.org/10.3103/s1066530725700012

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@article{jean2025,
  title        = {{Variable Selection in Multivariate Linear Regression Model for Spatially Dependent Data}},
  author       = {Jean Roland Ebende Penda et al.},
  journal      = {Mathematical Methods of Statistics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.3103/s1066530725700012},
}

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Variable Selection in Multivariate Linear Regression Model for Spatially Dependent Data

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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