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Estimation for partially linear additive regression with spatial data

Author

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  • Tang Qingguo

    (Nanjing University of Science and Technology)

  • Chen Wenyu

    (Nanjing University of Science and Technology)

Abstract

This paper studies a partially linear additive regression with spatial data. A new estimation procedure is developed for estimating the unknown parameters and additive components in regression. The proposed method is suitable for high dimensional data, there is no need to solve the restricted minimization problem and no iterative algorithms are needed. Under mild regularity assumptions, the asymptotic distribution of the estimator of the unknown parameter vector is established, the asymptotic distributions of the estimators of the unknown functions are also derived. Finite sample properties of our procedures are studied through Monte Carlo simulations. A real data example about spatial soil data is used to illustrate our proposed methodology.

Suggested Citation

  • Tang Qingguo & Chen Wenyu, 2022. "Estimation for partially linear additive regression with spatial data," Statistical Papers, Springer, vol. 63(6), pages 2041-2063, December.
    • Handle: RePEc:spr:stpapr:v:63:y:2022:i:6:d:10.1007_s00362-022-01326-8
    DOI: 10.1007/s00362-022-01326-8
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    References listed on IDEAS

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