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    Penalized estimation procedure for varying coefficient models, A

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    http://hdl.handle.net/10217/170359
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    Abstract
    Varying coefficient models are widely used for analyzing longitudinal data. Various methods for estimating coefficient functions have been developed over the years. We revisit the problem under the theme of functional sparsity. The problem of sparsity, including global sparsity and local sparsity, is a recurrent topic in nonparametric function estimation. A function has global sparsity if it is zero over the entire domain, and it indicates that the corresponding covariate is irrelevant to the response variable. A function has local sparsity if it is nonzero but remains zero for a set of intervals, ...
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    Author(s)
    Tu, Yan

    Advisor(s)
    Wang, Haonan

    Date Issued
    2015
    Format
    born digital; doctoral dissertations
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    • 2000-2019 - CSU Theses and Dissertations
    • Theses and Dissertations - Department of Statistics

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