A Study on the Structural Equation Modeling using the Nearest Neighbor Variance Estimation

• 발행기관: 한국자료분석학회
• 저자명: Sang Oak Dong Changwan Kang Kyu Kon Kim
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.14 No.4, 1759~1766쪽, 전체 8쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2012.08.30

영문 초록

The strength of nearest neighbor variance estimation (NNVE) is its robustness against a large proportion of noise points and against deviation from normality of the signal. Consistent results with this method have been established. Under some reasonable assumptions, it has been shown that the covariance estimate is bounded and that each point has only bounded influence on the final estimates. NNVE outperformed the popular minimum volume ellipsoid (MVE) estimator in simulation studies, with a big improvement when the proportion of outliers was very large. In this study, outlier data is applied to the robust covariance. In the result, for outlier data, the strong performance of NNVE is demonstrated. Thus, we propose the robust SEM by using the NNVE method in the case of existing outliers.

목차

1. Introduction

2. Nearest Neighbor Variance Estimation (NNVE)

3. Simulation

4. Conclusion and Proposal

Reference

키워드

SEM NNVE Outlier Model-fit Index Standard Estimate Standard Error