Shape constrained smothing using smoothing splines

    Research output: Contribution to journalArticlepeer-review

    34 Citations (Scopus)

    Abstract

    In some regression settings one would like to combine the flexibility of nonparametric smoothing with some prior knowledge about the regression curve. Such prior knowledge may come from a physical or economic theory, leading to shape constraints such as the underlying regression curve being positive, monotone, convex or concave. We propose a new method for calculating smoothing splines that fulfill these kinds of constraints. Our approach leads to a quadratic programming problem and the infinite number of constraints are replaced by a finite number of constraints that are chosen adaptively. We show that the resulting problem can be solved using the algorithm of Goldfarb and Idnani (1982, 1983) and illustrate our method on several real data sets.
    Original languageEnglish
    Pages (from-to)81-103
    JournalComputational Statistics
    Volume20
    Issue number1
    DOIs
    Publication statusPublished - 2005

    Fingerprint

    Dive into the research topics of 'Shape constrained smothing using smoothing splines'. Together they form a unique fingerprint.

    Cite this