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Nonparametric Conditional Risk Mapping Under Heteroscedasticity

Author

Listed:
  • Rubén Fernández-Casal

    (Universidade da Coruña)

  • Sergio Castillo-Páez

    (Universidad de las Fuerzas Armadas ESPE)

  • Mario Francisco-Fernández

    (Universidad de las Fuerzas Armadas ESPE)

Abstract

A nonparametric procedure to estimate the conditional probability that a nonstationary geostatistical process exceeds a certain threshold value is proposed. The method consists of a bootstrap algorithm that combines conditional simulation techniques with nonparametric estimations of the trend and the variability. The nonparametric local linear estimator, considering a bandwidth matrix selected by a method that takes the spatial dependence into account, is used to estimate the trend. The variability is modeled estimating the conditional variance and the variogram from corrected residuals to avoid the biasses. The proposed method allows to obtain estimates of the conditional exceedance risk in non-observed spatial locations. The performance of the approach is analyzed by simulation and illustrated with the application to a real data set of precipitations in the USA.Supplementary materials accompanying this paper appear on-line.

Suggested Citation

  • Rubén Fernández-Casal & Sergio Castillo-Páez & Mario Francisco-Fernández, 2024. "Nonparametric Conditional Risk Mapping Under Heteroscedasticity," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(1), pages 56-72, March.
    • Handle: RePEc:spr:jagbes:v:29:y:2024:i:1:d:10.1007_s13253-023-00555-0
    DOI: 10.1007/s13253-023-00555-0
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    References listed on IDEAS

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    3. Fan, Jianqing & Yao, Qiwei, 1998. "Efficient estimation of conditional variance functions in stochastic regression," LSE Research Online Documents on Economics 6635, London School of Economics and Political Science, LSE Library.
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