Sensitivity analysis in functional principal component analysis

Yoshihiro Yamanishi, Yutaka Tanaka

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)


    In the present paper empirical influence functions (EIFs) are derived for eigenvalues and eigenfunctions in functional principal component analysis in both cases where the smoothing parameter is fixed and unfixed. Based on the derived influence functions a sensitivity analysis procedure is proposed for detecting jointly as well as singly influential observations. A numerical example is given to show the usefulness of the proposed procedure. In dealing with the influence on the eigenfunctions two different kinds of influence statistics are introduced. One is based on the EIF for the coefficient vectors of the basis function expansion, and the other is based on the sampled vectors of the functional EIF. Under a certain condition it can be proved both kinds of statistics provide essentially equivalent results.

    Original languageEnglish
    Pages (from-to)311-326
    Number of pages16
    JournalComputational Statistics
    Issue number2
    Publication statusPublished - Dec 1 2005

    All Science Journal Classification (ASJC) codes

    • Statistics and Probability
    • Statistics, Probability and Uncertainty
    • Computational Mathematics


    Dive into the research topics of 'Sensitivity analysis in functional principal component analysis'. Together they form a unique fingerprint.

    Cite this