The Asymptotic Properties of Mean-centered Binned Kernel Density Estimator

• 발행기관: 한국자료분석학회
• 저자명: Changkon Hong Misook Yun
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.11 No.4, 1771~1783쪽, 전체 13쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2009.08.30

영문 초록

With the massive data set the kernel density estimator has the deficiency of huge computation time. In this situation, the reduction of the computation time is of crucial interest and hence the development of the binned kernel estimator is required. The general framework for binning rule and the resulting binned kernel estimator have been studied by some authors. In this paper we deal with another kind of binning rule, mean-centered binning, which is not included in the general framework for binning. We investigate the asymptotic properties of the mean-centered binned kernel density estimator. We also do the simulation study for the comparison with simple binned kernel estimator.

목차

1. Introduction

2. Binned kernel density estimators

3. Accuracy of mean-centered binned kernel density estimator

4. Concluding remarks

References

키워드

mean-centered binned kernel density estimator general framework for binning mean integrated squared error