Accuracy of Mean-centered Binned Kernel Density Estimator as an Approximation to the Ordinary Kernel Estimator

• 발행기관: 한국자료분석학회
• 저자명: Misook Yun Changkon Hong
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.13 No.4, 1677~1685쪽, 전체 9쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2011.08.30

영문 초록

When the data set is very large it takes huge computation time to obtain the kernel density estimator. In this case it is of crucial interest to reduce the computation time. One of the efficient way to achieve this goal is to use the binned kernel density estimator. Several binning rules and the resulting binned kernel estimator have been studied by some authors. In this paper we study, the accuracy of the mean-centered binned kernel estimator as a practical substitute for the original kernel density estimator. We investigate the asymptotic mean integrated squared difference between the mean-centered binned kernel density estimator and the original kernel estimator. We also do the simulation study for checking the accuracy.

목차

1. Introduction

2. A summary of previous work on binned kernel density estimators

3. Accuracy of mean-centered binned kernel density estimator as an approximation to the ordinary kernel estimator

4. Concluding remarks

References

키워드

mean-centered binning computation time general binning rule mean integrated squared difference