IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v61y2005i1p36-45.html
   My bibliography  Save this article

Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models

Author

Listed:
  • Montserrat Fuentes
  • Adrian E. Raftery

Abstract

No abstract is available for this item.

Suggested Citation

  • Montserrat Fuentes & Adrian E. Raftery, 2005. "Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models," Biometrics, The International Biometric Society, vol. 61(1), pages 36-45, March.
    • Handle: RePEc:bla:biomet:v:61:y:2005:i:1:p:36-45
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2005.030821.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. D. G. T. Denison & B. K. Mallick & A. F. M. Smith, 1998. "Automatic Bayesian curve fitting," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 333-350.
    2. Montserrat Fuentes, 2002. "Spectral methods for nonstationary spatial processes," Biometrika, Biometrika Trust, vol. 89(1), pages 197-210, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Joshua Warren & Montserrat Fuentes & Amy Herring & Peter Langlois, 2012. "Spatial-Temporal Modeling of the Association between Air Pollution Exposure and Preterm Birth: Identifying Critical Windows of Exposure," Biometrics, The International Biometric Society, vol. 68(4), pages 1157-1167, December.
    2. Xiaoyu Xiong & Benjamin D. Youngman & Theodoros Economou, 2021. "Data fusion with Gaussian processes for estimation of environmental hazard events," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    3. Price, Ilan & Fowkes, Jaroslav & Hopman, Daniel, 2019. "Gaussian processes for unconstraining demand," European Journal of Operational Research, Elsevier, vol. 275(2), pages 621-634.
    4. Mark A. Wolters & C. B. Dean, 2017. "Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 622-645, December.
    5. Veronica J. Berrocal & Alan E. Gelfand & David M. Holland, 2012. "Space-Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality," Biometrics, The International Biometric Society, vol. 68(3), pages 837-848, September.
    6. Guowen Huang & Patrick E. Brown & Sze Hang Fu & Hwashin Hyun Shin, 2022. "Daily mortality/morbidity and air quality: Using multivariate time series with seasonally varying covariances," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 148-174, January.
    7. C. Forlani & S. Bhatt & M. Cameletti & E. Krainski & M. Blangiardo, 2020. "A joint Bayesian space–time model to integrate spatially misaligned air pollution data in R‐INLA," Environmetrics, John Wiley & Sons, Ltd., vol. 31(8), December.
    8. Sujit K. Sahu & Alan E. Gelfand & David M. Holland, 2010. "Fusing point and areal level space–time data with application to wet deposition," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 77-103, January.
    9. Brian J. Reich & Howard H. Chang & Kristen M. Foley, 2014. "A spectral method for spatial downscaling," Biometrics, The International Biometric Society, vol. 70(4), pages 932-942, December.
    10. Wang, Craig & Furrer, Reinhard, 2021. "Combining heterogeneous spatial datasets with process-based spatial fusion models: A unifying framework," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    11. I. Gede Nyoman Mindra Jaya & Henk Folmer, 2022. "Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease," Journal of Geographical Systems, Springer, vol. 24(4), pages 527-581, October.
    12. Montserrat Fuentes & Hae-Ryoung Song & Sujit K. Ghosh & David M. Holland & Jerry M. Davis, 2006. "Spatial Association between Speciated Fine Particles and Mortality," Biometrics, The International Biometric Society, vol. 62(3), pages 855-863, September.
    13. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    14. Choi, Jungsoon & Fuentes, Montserrat & Reich, Brian J., 2009. "Spatial-temporal association between fine particulate matter and daily mortality," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2989-3000, June.
    15. Soumen Dey & Mohan Delampady & Ravishankar Parameshwaran & N. Samba Kumar & Arjun Srivathsa & K. Ullas Karanth, 2017. "Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 111-139, June.
    16. Daisuke Murakami & Morito Tsutsumi, 2015. "Area-to-point parameter estimation with geographically weighted regression," Journal of Geographical Systems, Springer, vol. 17(3), pages 207-225, July.
    17. Jonathan Rougier & Aoibheann Brady & Jonathan Bamber & Stephen Chuter & Sam Royston & Bramha Dutt Vishwakarma & Richard Westaway & Yann Ziegler, 2023. "The scope of the Kalman filter for spatio‐temporal applications in environmental science," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    18. Benjamin M. Taylor & Ricardo Andrade‐Pacheco & Hugh J. W. Sturrock, 2018. "Continuous inference for aggregated point process data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1125-1150, October.
    19. Justin J. Van Ee & Christian A. Hagen & David C. Pavlacky Jr. & Kent A. Fricke & Matthew D. Koslovsky & Mevin B. Hooten, 2023. "Melding wildlife surveys to improve conservation inference," Biometrics, The International Biometric Society, vol. 79(4), pages 3941-3953, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tatiyana V. Apanasovich & David Ruppert & Joanne R. Lupton & Natasa Popovic & Nancy D. Turner & Robert S. Chapkin & Raymond J. Carroll, 2008. "Aberrant Crypt Foci and Semiparametric Modeling of Correlated Binary Data," Biometrics, The International Biometric Society, vol. 64(2), pages 490-500, June.
    2. Marcelo Cunha & Dani Gamerman & Montserrat Fuentes & Marina Paez, 2017. "A non-stationary spatial model for temperature interpolation applied to the state of Rio de Janeiro," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 919-939, November.
    3. Pena, Daniel & Redondas, Dolores, 2006. "Bayesian curve estimation by model averaging," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 688-709, February.
    4. Villani, Mattias & Kohn, Robert & Giordani, Paolo, 2009. "Regression density estimation using smooth adaptive Gaussian mixtures," Journal of Econometrics, Elsevier, vol. 153(2), pages 155-173, December.
    5. Lindstrom, Mary J., 2002. "Bayesian estimation of free-knot splines using reversible jumps," Computational Statistics & Data Analysis, Elsevier, vol. 41(2), pages 255-269, December.
    6. Gianluca Frasso & Jonathan Jaeger & Philippe Lambert, 2016. "Parameter estimation and inference in dynamic systems described by linear partial differential equations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(3), pages 259-287, July.
    7. Elcin Koc & Cem Iyigun, 2014. "Restructuring forward step of MARS algorithm using a new knot selection procedure based on a mapping approach," Journal of Global Optimization, Springer, vol. 60(1), pages 79-102, September.
    8. Peter J. Brockwell & Yasumasa Matsuda, 2015. "Levy-driven CARMA Random Fields on Rn," TERG Discussion Papers 339, Graduate School of Economics and Management, Tohoku University.
    9. Martin Tingley & Benjamin Shaby, 2015. "Comments on: Comparing and selecting spatial predictors using local criteria," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 47-53, March.
    10. M. P. Wand, 2000. "A Comparison of Regression Spline Smoothing Procedures," Computational Statistics, Springer, vol. 15(4), pages 443-462, December.
    11. Joaquim Henriques Vianna Neto & Alexandra M. Schmidt & Peter Guttorp, 2014. "Accounting for spatially varying directional effects in spatial covariance structures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 103-122, January.
    12. Boracchi, Patrizia & Biganzoli, Elia & Marubini, Ettore, 2003. "Joint modelling of cause-specific hazard functions with cubic splines: an application to a large series of breast cancer patients," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 243-262, February.
    13. Smith, Michael & Kohn, Robert, 2000. "Nonparametric seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 98(2), pages 257-281, October.
    14. Yu Yue & Paul Speckman & Dongchu Sun, 2012. "Priors for Bayesian adaptive spline smoothing," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(3), pages 577-613, June.
    15. Basna, Rani & Nassar, Hiba & Podgórski, Krzysztof, 2022. "Data driven orthogonal basis selection for functional data analysis," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    16. Cardot, Hervé, 2002. "Spatially Adaptive Splines for Statistical Linear Inverse Problems," Journal of Multivariate Analysis, Elsevier, vol. 81(1), pages 100-119, April.
    17. Bozağaç, Doruk & Batmaz, İnci & Oğuztüzün, Halit, 2016. "Dynamic simulation metamodeling using MARS: A case of radar simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 124(C), pages 69-86.
    18. Feng Li & Mattias Villani, 2013. "Efficient Bayesian Multivariate Surface Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 706-723, December.
    19. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
    20. Janet Niekerk & Haakon Bakka & Håvard Rue, 2023. "Stable Non-Linear Generalized Bayesian Joint Models for Survival-Longitudinal Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 102-128, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:61:y:2005:i:1:p:36-45. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.