Type of Document	Dissertation
Author	Ivanescu, Andrada Eugenia
URN	etd-10292008-093112
Title	Revealing Sparse Signals In Functional Data
Degree	Doctor of Philosophy
Department	Statistics, Department of
Advisory Committee	Advisor Name Title Florentina Bunea Committee Co-Chair Marten Wegkamp Committee Co-Chair Myles Hollander Committee Member Xufeng Niu Committee Member Joshua Gert Outside Committee Member
Keywords	Sparse Signal Functional Data Analysis
Date of Defense	2008-10-24
Availability	unrestricted
Abstract My dissertation presents a novel statistical method to estimate a sparse signal in functional data and to construct confidence bands for the signal. Existing methods for inference for the mean function in this framework include smoothing splines and kernel estimates. Our methodology involves thresholding a least squares estimator, and the threshold level depends on the sources of variability that exist in this type of data. The proposed estimation method and the confidence bands successfully adapt to the sparsity of the signal. We present supporting evidence through simulations and applications to real datasets.
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