Smoothing: Computing Curves from Noisy Data

  • J.O Ramsay
  • Giles Hooker
  • Spencer Graves
Part of the Use R book series (USE R)


The previous two chapters have introduced the Matlab and R code needed to specify basis function systems and then to define curves by combining these coefficient arrays. For example, we saw how to construct a basis object such as heightbasis to define growth curves and how to combine it with a matrix of coefficients such as heightcoef so as to define growth functional data objects such as were plotted in Figure 1.1.

We now turn to methods for computing these coefficients with more careful consideration of measurement error. For example, how do we compute these coefficients to obtain an optimal fit to data such as the height measurements for 54 girls in the Berkeley growth study stored in the 31 by 54 matrix that we name heightmat? Or how do we replace the rather noisy mean daily precipitation observations by smooth curves?


Noisy Data Regression Spline Linear Differential Operator Computing Curve Roughness Penalty 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  1. 1.OttawaCanada
  2. 2.Department of Biological Statistics & Computational BiologyCornell UniversityIthacaUSA
  3. 3.Productive Systems EngineeringSan JoseUSA

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