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On nonparametric inference for spatial regression models under domain expanding and infill asymptotics

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  • Kurisu, Daisuke

Abstract

In this paper, we develop nonparametric inference on spatial regression models as an extension of Lu and Tjøstheim (2014), which develops nonparametric inference on density functions of stationary spatial processes under domain expanding and infill (DEI) asymptotics. In particular, we derive multivariate central limit theorems of mean and variance functions of nonparametric spatial regression models. Built upon those results, we propose a method to construct confidence bands for mean and variance functions.

Suggested Citation

  • Kurisu, Daisuke, 2019. "On nonparametric inference for spatial regression models under domain expanding and infill asymptotics," Statistics & Probability Letters, Elsevier, vol. 154(C), pages 1-1.
    • Handle: RePEc:eee:stapro:v:154:y:2019:i:c:16
    DOI: 10.1016/j.spl.2019.06.019
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    References listed on IDEAS

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    Cited by:

    1. Tao Chen & Yixuan Li & Renfang Tian, 2023. "A Functional Data Approach for Continuous-Time Analysis Subject to Modeling Discrepancy under Infill Asymptotics," Mathematics, MDPI, vol. 11(20), pages 1-27, October.
    2. Francis K.C. Hui & Nicole A. Hill & A.H. Welsh, 2022. "Assuming independence in spatial latent variable models: Consequences and implications of misspecification," Biometrics, The International Biometric Society, vol. 78(1), pages 85-99, March.

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