Central limit theorem for nonparametric regression under dependent data

HKUST Electronic Theses

Central limit theorem for nonparametric regression under dependent data

by Mok Kit Ying

THESIS 2003

M.Phil. Mathematics

vi, 44 leaves ; 30 cm

Abstract
This paper is concerned with estimating nonparametric regression function g on the basis of noisy observations taken at nonrandom design points x_ni. Such noises ξ_i’s are linear combinations of weakly dependent random variables satisfying certain assumptions and weighted sum of negatively associated random variables respectively. The main result of this study is that, the normalized version of a general linear smoother of the form g_n(x) = ∑_iⁿ₌₁ ω_ni(x)Y_ni are asymptotically normal for these two cases.

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Details

Collection HKUST Electronic Theses Degree M.Phil. Department Mathematics Authors Mok, Kit Ying Subjects Central limit theorem Regression analysis Language English Call number Thesis MATH 2003 Mok DOI 10.14711/thesis-b803251

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Central limit theorem for nonparametric regression under dependent data

by Mok Kit Ying

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