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Spline Smoothing over Difficult Regions

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Author Info
Siem Jan Koopman () (VU University Amsterdam)
Soon Yip Wong () (VU University Amsterdam)

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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.

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Publisher Info
Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 08-114/4.

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Date of creation: 18 Nov 2008
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Handle: RePEc:dgr:uvatin:20080114

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Web page: http://www.tinbergen.nl/

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Related research
Keywords: Bivariate smoothing; Geo-statistics; Missing observations; Smoothing spline model; State space methods;

Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, vol. 27(7), pages 1317-1333, May. [Downloadable!] (restricted)
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  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. [Downloadable!] (restricted)
  3. Tim Ramsay, 2002. "Spline smoothing over difficult regions," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 307-319. [Downloadable!] (restricted)
  4. Graham W. Horgan, 1999. "Using wavelets for data smoothing: a simulation study," Journal of Applied Statistics, Taylor and Francis Journals, vol. 26(8), pages 923-932, December. [Downloadable!] (restricted)
  5. E. E. Kammann & M. P. Wand, 2003. "Geoadditive models," Journal Of The Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 1-18. [Downloadable!] (restricted)
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