IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i4p1167-1178.html
   My bibliography  Save this article

Robust smoothing of gridded data in one and higher dimensions with missing values

Author

Listed:
  • Garcia, Damien

Abstract

A fully automated smoothing procedure for uniformly sampled datasets is described. The algorithm, based on a penalized least squares method, allows fast smoothing of data in one and higher dimensions by means of the discrete cosine transform. Automatic choice of the amount of smoothing is carried out by minimizing the generalized cross-validation score. An iteratively weighted robust version of the algorithm is proposed to deal with occurrences of missing and outlying values. Simplified Matlab codes with typical examples in one to three dimensions are provided. A complete user-friendly Matlab program is also supplied. The proposed algorithm, which is very fast, automatic, robust and requiring low storage, provides an efficient smoother for numerous applications in the area of data analysis.

Suggested Citation

  • Garcia, Damien, 2010. "Robust smoothing of gridded data in one and higher dimensions with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1167-1178, April.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:4:p:1167-1178
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00349-1
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Weinert, Howard L., 2007. "Efficient computation for Whittaker-Henderson smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 959-974, October.
    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. Jinquan Ai & Chao Zhang & Lijuan Chen & Dajun Li, 2020. "Mapping Annual Land Use and Land Cover Changes in the Yangtze Estuary Region Using an Object-Based Classification Framework and Landsat Time Series Data," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    2. Felix Wellschmied, 2021. "The welfare effects of asset mean‐testing income support," Quantitative Economics, Econometric Society, vol. 12(1), pages 217-249, January.
    3. Davide Pigoli & Pantelis Z. Hadjipantelis & John S. Coleman & John A. D. Aston, 2018. "The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1103-1145, November.
    4. Goyal, Rahul & Gandhi, B.K. & Cervantes, Michel J., 2018. "PIV measurements in Francis turbine – A review and application to transient operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2976-2991.
    5. Michael F Bonner & Russell A Epstein, 2018. "Computational mechanisms underlying cortical responses to the affordance properties of visual scenes," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-31, April.
    6. Dong, Haitao & Shen, Xiaohong & He, Ke & Wang, Haiyan, 2020. "Nonlinear filtering effects of intrawell matched stochastic resonance with barrier constrainted duffing system for ship radiated line signature extraction," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    7. Hiroshi Yamada, 2021. "Geary’s c and Spectral Graph Theory," Mathematics, MDPI, vol. 9(19), pages 1-23, October.
    8. Chuanfa Chen & Yanyan Li & Na Zhao & Jinyun Guo & Guolin Liu, 2017. "A fast and robust interpolation filter for airborne lidar point clouds," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-20, May.
    9. Adrien Méry & Artur Ruppel & Jean Revilloud & Martial Balland & Giovanni Cappello & Thomas Boudou, 2023. "Light-driven biological actuators to probe the rheology of 3D microtissues," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    10. Klaus Ackermann & Simon D Angus & Paul A Raschky, 2017. "The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations," Papers 1701.05632, arXiv.org.
    11. Soza-Amigo, Sergio & Aroca, Patricio, 2015. "Identifying a Country As ¨Developed¨ Based On Their Structural Similarities," MPRA Paper 77421, University Library of Munich, Germany.
    12. Klaus Ackermann & Simon D Angus & Paul A Raschky, 2020. "Estimating Sleep and Work Hours from Alternative Data by Segmented Functional Classification Analysis, SFCA," SoDa Laboratories Working Paper Series 2020-04, Monash University, SoDa Laboratories.
    13. Klaus Ackermann & Simon D. Angus & Paul A. Raschky, 2020. "Estimating Sleep & Work Hours from Alternative Data by Segmented Functional Classification Analysis (SFCA)," Papers 2010.08102, arXiv.org.
    14. Benjamin J. Davison & Anna E. Hogg & Richard Rigby & Sanne Veldhuijsen & Jan Melchior Wessem & Michiel R. Broeke & Paul R. Holland & Heather L. Selley & Pierre Dutrieux, 2023. "Sea level rise from West Antarctic mass loss significantly modified by large snowfall anomalies," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    15. Feng, Runhai & Nasser, Saleh, 2024. "Stochastic inversion of fracture networks using the reversible jump Markov chain Monte Carlo algorithm," Energy, Elsevier, vol. 301(C).

    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. Ruixue Du & Hiroshi Yamada, 2020. "Principle of Duality in Cubic Smoothing Spline," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    2. Garcin, Matthieu, 2017. "Estimation of time-dependent Hurst exponents with variational smoothing and application to forecasting foreign exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 462-479.
    3. Weinert, Howard L., 2009. "A fast compact algorithm for cubic spline smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 932-940, February.
    4. Hiroshi Yamada, 2023. "Quantile regression version of Hodrick–Prescott filter," Empirical Economics, Springer, vol. 64(4), pages 1631-1645, April.
    5. Hiroshi Yamada & Ruoyi Bao, 2022. "$$\ell _{1}$$ ℓ 1 Common Trend Filtering," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1005-1025, March.
    6. Zihan Jin & Hiroshi Yamada, 2024. "Boosted Whittaker–Henderson Graduation," Mathematics, MDPI, vol. 12(21), pages 1-18, October.
    7. Dermoune Azzouz & Djehiche Boualem & Rahmania Nadji, 2009. "Multivariate Extension of the Hodrick-Prescott Filter-Optimality and Characterization," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(3), pages 1-35, May.

    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:eee:csdana:v:54:y:2010:i:4:p:1167-1178. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.