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    • Graduate School - MU Theses and Dissertations (MU)
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    • 2008 Dissertations (MU)
    • 2008 MU dissertations - Access restricted to MU
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    Spatially adaptive priors for regression and spatial modeling

    Yue, Yu, 1981-
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    [PDF] research.pdf (4.198Mb)
    Date
    2008
    Format
    Thesis
    Metadata
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    Abstract
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] The smoothing splines and penalized regression splines (P-splines) are popular nonparametric regression methods for curve fitting problems, and the thin-plate spline (a two-dimensional version of smoothing spline) is a well-known surface fitting method and has been used intensively in spatial smoothing area. These spline basis methods, however, both suffer from having only one global smoothing parameter that controls the degrees of smoothness for the fit. It is especially an issue when the function of interest is highly varying through the input space. To overcome this inadequacy, we develop a class of priors for adaptive spline smoothing. These priors extend certain stochastic process by using a spatially adaptive variance component and taking a further process as prior for this variance function.
    URI
    https://doi.org/10.32469/10355/6059
    https://hdl.handle.net/10355/6059
    Degree
    Ph. D.
    Thesis Department
    Statistics (MU)
    Rights
    Access is limited to the campus of the University of Missouri--Columbia.
    Collections
    • 2008 MU dissertations - Access restricted to MU
    • Statistics electronic theses and dissertations (MU)

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