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Ohio State University logo STAT 763 Nonparametric function estimation Home

Course information

  1. Description:

    Statistics 763 aims to introduce nonparametric function estimation method with roughness penalty. Starting from smoothing splines for univariate data, a unified framework will be developed for flexible model building of multivariate data with both Gaussian and non-Gaussian responses. Mathematical formulation of smoothing splines, reproducing kernel Hilbert space methods, selection of smoothing parameter, computation, and their applications will be treated in detail.

  2. Lecture: MW 2:00PM -3:18PM in Baker Systems Engineering (BE) 188.
  3. Text: Smoothing Spline ANOVA Models, by Chong Gu.
    References : Spline Models for Observational Data by Grace Wahba.
    Nonparametric Regression and Generalized Linear Models by Peter Green and Bernard Silverman.
    The books are on reserve in Science Engineering Library (SEL).
  4. Instructor:
    Yoonkyung Lee
    Office: 440B Cockins Hall
    Phone: 292-9495
    Office Hours: W 3:30 - 4:30PM F 2:00 - 3:00PM or by appointment
    Email: yklee at stat domain [when e-mailing, replace 'at stat domain' by @stat.osu.edu]
  5. Grader:
    Kazuki Uematsu
    Office: 454 Math Building
    Office Hours: by appointment
    Email: kazuki at stat domain

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