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A complementary study on analysis of simulation results using statistical models

통계모형을 이용하여 모의실험 결과 분석하기에 대한 보완연구

  • Kim, Ji-Hyun (Department of Statistics and Actuarial Science, Soongsil University) ;
  • Kim, Bongseong (Department of Statistics and Actuarial Science, Soongsil University)
  • 김지현 (숭실대학교 정보통계보험수리학과) ;
  • 김봉성 (숭실대학교 정보통계보험수리학과)
  • Received : 2022.05.10
  • Accepted : 2022.06.25
  • Published : 2022.08.31

Abstract

Simulation studies are often conducted when it is difficult to compare the performance of nonparametric estimators theoretically. Kim and Kim (2021) showed that more systematic and accurate comparisons can be made if you analyze the simulation results using a regression model,. This study is a complementary study on Kim and Kim (2021). In the variance-covariance matrix for the error term of the regression model, only heteroscedasticity was considered and covariance was ignored in the previous study. When covariance is considered together with the heteroscedasticity, the variance-covariance matrix becomes a block diagonal matrix. In this study, a method of estimating and using the block diagonal variance-covariance matrix for the analysis was presented. This allows you to find more pairs of estimators with significant performance differences while ensuring the nominal confidence level.

비모수적 추정량의 성능을 이론적으로 비교하기 힘들 때 흔히 모의실험을 실시한다. 다양한 실험조건에서 여러 추정량에 대해 얻어진 모의실험 결과를 회귀모형을 이용해 분석하면 보다 체계적이고 정확한 비교를 할 수 있다는 것을 Kim과 Kim (2021)에서 보였다. 이 연구는 Kim과 Kim (2021)에 대한 후속연구이자 보완연구이다. 회귀모형의 오차항에 대한 분산공분산행렬에서 이분산성만 고려하고 공분산을 선행연구에서 무시했는데, 공분산을 고려하게 되면 분산공분산행렬은 블록대각행렬이 된다. 본 연구에서 블록대각행렬인 분산공분산행렬을 추정하여 분석에 이용하는 방법을 제시하였다. 이렇게 하면 명목신뢰수준을 보장하면서 유의하게 성능 차이가 나는 추정량 짝을 더 잘 찾을 수 있다는 것도 보였다.

Keywords

References

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