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Nonparametric prediction for univariate spatial data: Methods and applications

Author

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  • Rodrigo García Arancibia
  • Pamela Llop
  • Mariel Lovatto
Abstract
We introduce five nonparametric kriging‐type predictors for spatial data where only the variable of interest, without covariates, is recorded. The proposed methods seek to fully exploit the information contained in the spatial closeness and also in the similarity between neighbourhoods of the variable of interest. This is managed using different combinations of kernels (one or two kernels), and different combinations of distances (multiplicative and additive). The good performance of the proposed methods is shown via simulation studies and housing price prediction applications. Este estudio introduce cinco predictores no paramétricos de interpolación de tipo Kriging para datos espaciales en los que sólo se registra la variable de interés, sin covariables. Los métodos propuestos tratan de explotar al máximo la información contenida en la cercanía espacial y también en la similitud entre vecindarios de la variable de interés. Para ello se utilizan distintas combinaciones de núcleos (uno o dos núcleos) y distintas combinaciones de distancias (multiplicativas y aditivas). El buen desempeño de los métodos propuestos se demuestra mediante estudios de simulación y aplicaciones para la predicción del precio de la vivienda. 関心のある変数のみが共変量を含まずに記録される、空間データを分析する5つのノンパラメトリック・クリギング法タイプの予測因子を導入する。この方法は、空間的近接性及び関心のある変数の近似値間の類似性に含まれる情報を十分に活用しようとするものである。これは、異なるカーネルの組み合わせ(1つまたは2つのカーネル)、および異なる距離の組み合わせ(乗法と加法)を使用して実施される。提案した方法は、シミュレーション研究と住宅価格予測への応用では、良好な性能が示された。

Suggested Citation

  • Rodrigo García Arancibia & Pamela Llop & Mariel Lovatto, 2023. "Nonparametric prediction for univariate spatial data: Methods and applications," Papers in Regional Science, Wiley Blackwell, vol. 102(3), pages 635-672, June.
    • Handle: RePEc:bla:presci:v:102:y:2023:i:3:p:635-672
    DOI: 10.1111/pirs.12735
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    References listed on IDEAS

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