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Spatiotemporal quantile regression for detecting distributional changes in environmental processes

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  • Brian J. Reich

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  • Brian J. Reich, 2012. "Spatiotemporal quantile regression for detecting distributional changes in environmental processes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(4), pages 535-553, August.
    • Handle: RePEc:bla:jorssc:v:61:y:2012:i:4:p:535-553
    DOI: j.1467-9876.2011.01025.x
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    File URL: http://hdl.handle.net/10.1111/j.1467-9876.2011.01025.x
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    Citations

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    Cited by:

    1. Matteo Coronese & Francesco Lamperti & Francesca Chiaromonte & Andrea Roventini, 2018. "Natural Disaster Risk and the Distributional Dynamics of Damages," LEM Papers Series 2018/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Chao, Shih-Kang & Härdle, Wolfgang K. & Yuan, Ming, 2021. "Factorisable Multitask Quantile Regression," Econometric Theory, Cambridge University Press, vol. 37(4), pages 794-816, August.
    3. Das, Priyam & Ghosal, Subhashis, 2018. "Bayesian non-parametric simultaneous quantile regression for complete and grid data," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 172-186.
    4. Zhen Yu & Keming Yu & Wolfgang K. Härdle & Xueliang Zhang & Kai Wang & Maozai Tian, 2022. "Bayesian spatio‐temporal modeling for the inpatient hospital costs of alcohol‐related disorders," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 644-667, December.
    5. Viviana Carcaiso & Leonardo Grilli, 2023. "Quantile regression for count data: jittering versus regression coefficients modelling in the analysis of credits earned by university students after remote teaching," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1061-1082, October.
    6. Das, Priyam & Ghosal, Subhashis, 2017. "Bayesian quantile regression using random B-spline series prior," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 121-143.
    7. Brian J. Reich & Luke B. Smith, 2013. "Bayesian Quantile Regression for Censored Data," Biometrics, The International Biometric Society, vol. 69(3), pages 651-660, September.
    8. Fabrizi, Enrico & Salvati, Nicola & Trivisano, Carlo, 2020. "Robust Bayesian small area estimation based on quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    9. repec:hum:wpaper:sfb649dp2016-057 is not listed on IDEAS
    10. Poppick, Andrew & McKinnon, Karen A., 2020. "Observation-based Simulations of Humidity and Temperature Using Quantile Regression," Earth Arxiv bmskp, Center for Open Science.
    11. Karen A. McKinnon & Andrew Poppick, 2020. "Estimating Changes in the Observed Relationship Between Humidity and Temperature Using Noncrossing Quantile Smoothing Splines," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 292-314, September.
    12. Sottile, Gianluca & Frumento, Paolo, 2022. "Robust estimation and regression with parametric quantile functions," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    13. Niyibizi, Bart & Brorsen, Wade & Park, Eunchun, 2018. "Using Bayesian Kriging for Spatial Smoothing of Trends in the Means and Variances of Crop Yield Densities," 2018 Annual Meeting, August 5-7, Washington, D.C. 274403, Agricultural and Applied Economics Association.
    14. Ramsey, A., 2018. "Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277253, International Association of Agricultural Economists.
    15. Priyam Das & Christine B. Peterson & Yang Ni & Alexandre Reuben & Jiexin Zhang & Jianjun Zhang & Kim‐Anh Do & Veerabhadran Baladandayuthapani, 2023. "Bayesian hierarchical quantile regression with application to characterizing the immune architecture of lung cancer," Biometrics, The International Biometric Society, vol. 79(3), pages 2474-2488, September.
    16. Curtis B. Storlie & Brian J. Reich & William N. Rust & Lawrence O. Ticknor & Amanda M. Bonnie & Andrew J. Montoya & Sarah E. Michalak, 2017. "Spatiotemporal Modeling of Node Temperatures in Supercomputers," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 92-108, January.
    17. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
    18. Brian J. Reich & Shu Yang & Yawen Guan & Andrew B. Giffin & Matthew J. Miller & Ana Rappold, 2021. "A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications," International Statistical Review, International Statistical Institute, vol. 89(3), pages 605-634, December.
    19. Chawarat Rotejanaprasert & Andrew B. Lawson, 2018. "A Bayesian Quantile Modeling for Spatiotemporal Relative Risk: An Application to Adverse Risk Detection of Respiratory Diseases in South Carolina, USA," IJERPH, MDPI, vol. 15(9), pages 1-15, September.
    20. Paolo Frumento & Nicola Salvati, 2021. "Parametric modeling of quantile regression coefficient functions with count data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1237-1258, October.
    21. Rahim Alhamzawi, 2016. "Bayesian Analysis of Composite Quantile Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 358-373, October.
    22. Yunwen Yang & Huixia Judy Wang & Xuming He, 2016. "Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood," International Statistical Review, International Statistical Institute, vol. 84(3), pages 327-344, December.

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