Statistical Modelling 8 (2008), 367–383

Sharpening P-spline signal regression

Bin Li
Department of Experimental Statistics, Louisiana State University
Room 61, Agricultural Administration Building
Baton Rouge, LA 70803-5606
U.S.A.
eMail: bli@lsu.edu

Brian D. Marx
Department of Experimental Statistics, Louisiana State University
U.S.A.

Abstract:

We propose two variations of P-spline signal regression: space-varying penalization signal regression (SPSR) and additive polynomial signal regression (APSR). SPSR uses space-varying roughness penalty according to the estimated coefficients from the partial least-squares (PLS) regression, while APSR expands the linear basis to polynomial bases. SPSR and APSR are motivated in the following two scenarios, respectively: (i) some region(s) of the regressor channels contain more useful information for prediction than others and (ii) the relationship between the response and regressor channels is highly nonlinear. We also extend the methods to the generalized linear regression setting. As illustration, we apply the methods to two published data sets showing highly competitive performance.

Keywords:

multivariate calibration; P-splines; partial least squares

Downloads:

Example data and R-code in zipped archive.

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