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Smooth Backfitting in Errors-in-Variables Additive Models

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Authors
한경희
Advisor
박병욱
Major
자연과학대학 통계학과
Issue Date
2017-02
Publisher
서울대학교 대학원
Keywords
nonparametric additive modelsdecovolutionkernel smoothing estimationsmooth backfitting
Description
학위논문 (박사)-- 서울대학교 대학원 : 통계학과, 2017. 2. 박병욱.
Abstract
We study nonparametric additive regression models when noisy covariates are observed within measurement errors. Based on deconvolution techniques, we construct an iterative algorithm for smooth backfitting of additive function in the presence of errors-in-variables. We show that the smooth backfitting achieves univariate accuracy of the standard deconvolution for estimating each component function under certain conditions. Deconvolving noise on backfitting is confined into negligible magnitude that rate of convergence of the proposed estimator is accelerated when the smoothness of measurement errors falls into a certain range. We also present finite sample performance of the deconvolution smooth backfitting in comparison with a naive application of the standard smooth backfitting ignoring measurement errors. Monte Carlo simulation is demonstrated that our method gives smaller mean integrated squared errors than the naive one in average.
Language
English
URI
https://hdl.handle.net/10371/121164
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Ph.D. / Sc.D._통계학과)
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