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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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    1. Siddhartha Nandy & Chae Young Lim & Tapabrata Maiti, 2017. "Additive model building for spatial regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 779-800, June.
    2. Al-Sulami, Dawlah & Jiang, Zhenyu & Lu, Zudi & Zhu, Jun, 2017. "Estimation for semiparametric nonlinear regression of irregularly located spatial time-series data," Econometrics and Statistics, Elsevier, vol. 2(C), pages 22-35.
    3. Athreya, Krishna B. & Pantula, Sastry G., 1986. "A note on strong mixing of ARMA processes," Statistics & Probability Letters, Elsevier, vol. 4(4), pages 187-190, June.
    4. Lee, Y. K. & Choi, H. & Park, B. U. & Yu, K. S., 2004. "Local likelihood density estimation on random fields," Statistics & Probability Letters, Elsevier, vol. 68(4), pages 347-357, July.
    5. Hua Liang & Sally W. Thurston & David Ruppert & Tatiyana Apanasovich & Russ Hauser, 2008. "Additive partial linear models with measurement errors," Biometrika, Biometrika Trust, vol. 95(3), pages 667-678.
    6. Li, Qi, 2000. "Efficient Estimation of Additive Partially Linear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 1073-1092, November.
    7. Marc Hallin & Zudi Lu & Lanh T. Tran, 2001. "Density estimation for spatial linear processes," ULB Institutional Repository 2013/2109, ULB -- Universite Libre de Bruxelles.
    8. Tran, Lanh Tat, 1990. "Kernel density estimation on random fields," Journal of Multivariate Analysis, Elsevier, vol. 34(1), pages 37-53, July.
    9. Shan Yu & Guannan Wang & Li Wang & Chenhui Liu & Lijian Yang, 2020. "Estimation and Inference for Generalized Geoadditive Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 761-774, April.
    10. Marc Hallin & Zudi Lu & Lanh T. Tran, 2004. "Local linear spatial regression," ULB Institutional Repository 2013/2131, ULB -- Universite Libre de Bruxelles.
    11. Tim Ramsay, 2002. "Spline smoothing over difficult regions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 307-319, May.
    12. Jing Yang & Hu Yang & Fang Lu, 2019. "Rank-based shrinkage estimation for identification in semiparametric additive models," Statistical Papers, Springer, vol. 60(4), pages 1255-1281, August.
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