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Spline Smoothing over Difficult Regions

Author

Listed:
  • Siem Jan Koopman

    (VU University Amsterdam)

  • Soon Yip Wong

    (VU University Amsterdam)

Abstract

We consider the problem of smoothing data on two-dimensional grids with holes or gaps. Such grids are often referred to as difficult regions. Since the data is not observed on these locations, the gap is not part of the domain. We cannot apply standard smoothing methods since they smooth over and across difficult regions. More unfavorable properties of standard smoothers become visible when the data is observed on an irregular grid in a non-rectangular domain. In this paper, we adopt smoothing spline methods within a state space framework to smooth data on one- or two-dimensional grids with difficult regions. We make a distinction between two types of missing observations to handle the irregularity of the grid and to ensure that no smoothing takes place over and across the difficult region. For smoothing on two-dimensional grids, we introduce a two-step spline smoothing method. The proposed solution applies to all smoothing methods that can be represented in a state space framework. We illustrate our methods for three different cases of interest.

Suggested Citation

  • Siem Jan Koopman & Soon Yip Wong, 2008. "Spline Smoothing over Difficult Regions," Tinbergen Institute Discussion Papers 08-114/4, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20080114
    as

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    File URL: https://papers.tinbergen.nl/08114.pdf
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    References listed on IDEAS

    as
    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    2. Gao F. & Wahba G. & Klein R. & Klein B., 2001. "Smoothing Spline ANOVA for Multivariate Bernoulli Observations With Application to Ophthalmology Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 127-160, March.
    3. Yuedong Wang & Chunlei Ke & Morton B. Brown, 2003. "Shape-Invariant Modeling of Circadian Rhythms with Random Effects and Smoothing Spline ANOVA Decompositions," Biometrics, The International Biometric Society, vol. 59(4), pages 804-812, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Bivariate smoothing; Geo-statistics; Missing observations; Smoothing spline model; State space methods;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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