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Discussion on “Spatial+: A novel approach to spatial confounding” by Emiko Dupont, Simon N. Wood, and Nicole H. Augustin

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  • Isa Marques
  • Thomas Kneib

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  • Isa Marques & Thomas Kneib, 2022. "Discussion on “Spatial+: A novel approach to spatial confounding” by Emiko Dupont, Simon N. Wood, and Nicole H. Augustin," Biometrics, The International Biometric Society, vol. 78(4), pages 1295-1299, December.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:4:p:1295-1299
    DOI: 10.1111/biom.13650
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    References listed on IDEAS

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    1. Hauke Thaden & Thomas Kneib, 2018. "Structural Equation Models for Dealing With Spatial Confounding," The American Statistician, Taylor & Francis Journals, vol. 72(3), pages 239-252, July.
    2. Garritt L. Page & Yajun Liu & Zhuoqiong He & Donchu Sun, 2017. "Estimation and Prediction in the Presence of Spatial Confounding for Spatial Linear Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 780-797, September.
    3. Guillermo Briseño Sanchez & Maike Hohberg & Andreas Groll & Thomas Kneib, 2020. "Flexible instrumental variable distributional regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1553-1574, October.
    4. Matteo Fasiolo & Simon N. Wood & Margaux Zaffran & Raphaël Nedellec & Yannig Goude, 2021. "Fast Calibrated Additive Quantile Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(535), pages 1402-1412, July.
    5. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    6. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
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