Browse

Positive Definite Nonparametric Regression with Application to Covariance Function Estimation
양정치 비모수 회귀모형과 공분산 함수 추정에 대한 응용

Cited 0 time in Web of Science Cited 0 time in Scopus
Authors
강명종
Advisor
박병욱
Major
자연과학대학 통계학과
Issue Date
2017-08
Publisher
서울대학교 대학원
Keywords
Nonparametric regressionkernelpositive definitenessisotropymonotonicitycovariancevariogramestimation of distribution algorithm
Description
학위논문 (석사)-- 서울대학교 대학원 자연과학대학 통계학과, 2017. 8. 박병욱.
Abstract
We propose a new nonparametric regression method subject to the positive definiteness constraint for estimating the covariance function of a stationary stochastic process. Our method ensures that the estimator can satisfy positive definiteness, as well as both isotropy and monotonicity. Also, unlike previous methods, our estimators can be computed automatically with cross validation. To achieve these advantages, we define our estimators by taking the integral transform of kernel distribution estimators and suggest universal estimation algorithm based on the framework of Estimation of Distribution Algorithms (EDAs). A small simulation study is performed that demonstrates the usefulness of our method to estimate the covariance function more robustly than other typical nonparametric regression estimators. An application to the sic.100 dataset is also presented.
Language
English
URI
http://hdl.handle.net/10371/138092
Files in This Item:
Appears in Collections:
College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse