On the Weak Convergence and Central Limit Theorem of Blurring and Nonblurring Processes with Application to Robust Location Estimation
Statistics Theory
2015-01-28 v3 Probability
Statistics Theory
Abstract
This article studies the weak convergence and associated Central Limit Theorem for blurring and nonblurring processes. Then, they are applied to the estimation of location parameter. Simulation studies show that the location estimation based on the convergence point of blurring process is more robust and often more efficient than that of nonblurring process.
Keywords
Cite
@article{arxiv.1412.1411,
title = {On the Weak Convergence and Central Limit Theorem of Blurring and Nonblurring Processes with Application to Robust Location Estimation},
author = {Ting-Li Chen and Hironori Fujisawa and Su-Yun Huang and Chii-Ruey Hwang},
journal= {arXiv preprint arXiv:1412.1411},
year = {2015}
}