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Comments on: Process modeling for slope and aspect with application to elevation data maps

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  • Erin M. Schliep

    () (University of Missouri)

Abstract

Abstract The authors should be commended on their methodological development of stochastic processes for slope and aspect. Their development of basic distribution theory needed to study these two processes, and the sufficient conditions that ensure independence and non-informative induced priors, provide a thorough contribution to the collection of work on gradient processes. The fully model-based approach for inference for slope and aspect enables the propagation of uncertainty to environmental process models of interest that would use these variables as explanatory variables in a regression. I greatly appreciate the opportunity to comment on this exciting work and offer some additional model considerations and applications. Specifically, I reaffirm the importance of scalability of the methodology to large datasets, offering a few considerations with regard to model specification. Next, I discuss the unique challenges of using the predictive distributions of the slope and aspect processes as input variables in spatial regression models. Finally, I offer possible applications and extensions of this work that might provide innovative insights into environmental processes.

Suggested Citation

  • Erin M. Schliep, 2018. "Comments on: Process modeling for slope and aspect with application to elevation data maps," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 778-782, December.
  • Handle: RePEc:spr:testjl:v:27:y:2018:i:4:d:10.1007_s11749-018-0620-4
    DOI: 10.1007/s11749-018-0620-4
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    References listed on IDEAS

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    1. repec:taf:jnlasa:v:111:y:2016:i:514:p:800-812 is not listed on IDEAS
    2. Kaufman, Cari G. & Schervish, Mark J. & Nychka, Douglas W., 2008. "Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1545-1555.
    3. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    4. 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.
    5. John Hughes & Murali Haran, 2013. "Dimension reduction and alleviation of confounding for spatial generalized linear mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 139-159, January.
    6. Ephraim M. Hanks & Erin M. Schliep & Mevin B. Hooten & Jennifer A. Hoeting, 2015. "Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification," Environmetrics, John Wiley & Sons, Ltd., vol. 26(4), pages 243-254, June.
    7. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
    8. Hodges, James S. & Reich, Brian J., 2010. "Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love," The American Statistician, American Statistical Association, vol. 64(4), pages 325-334.
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