• Title/Summary/Keyword: 로버스트 추정법

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A Comparison of Robust Parameter Estimations for Autoregressive Models (자기회귀모형에서의 로버스트한 모수 추정방법들에 관한 연구)

  • Kang, Hee-Jeong;Kim, Soon-Young
    • Journal of the Korean Data and Information Science Society
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    • v.11 no.1
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    • pp.1-18
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    • 2000
  • In this paper, we study several parameter estimation methods used for autoregressive processes and compare them in view of forecasting. The least square estimation, least absolute deviation estimation, robust estimation are compared through Monte Carlo simulations.

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Doubly Robust Imputation Using Auxiliary Information (보조 정보에 의한 이중적 로버스트 대체법)

  • Park, Hyeon-Ah;Jeon, Jong-Woo;Na, Seong-Ryong
    • Communications for Statistical Applications and Methods
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    • v.18 no.1
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    • pp.47-55
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    • 2011
  • Ratio and regression imputations depend on the model of a survey variable and the relation between the survey variable and auxiliary variables. If the model is not true, the unbiasedness of the estimator using the ratio or regression imputation cannot be guaranteed. In this paper, we develop the doubly robust imputation, which satisfies the approximate unbiasedness of the estimator, whether the model assumption is valid or not. The proposed imputation increases the efficiency of estimation by using the population information of the auxiliary variables. The simulation study establishes the theoretical results of this paper.

Minimum Density Power Divergence Estimation for Normal-Exponential Distribution (정규-지수분포에 대한 최소밀도함수승간격 추정법)

  • Pak, Ro Jin
    • The Korean Journal of Applied Statistics
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    • v.27 no.3
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    • pp.397-406
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    • 2014
  • The minimum density power divergence estimation has been a popular topic in the field of robust estimation for since Basu et al. (1988). The minimum density power divergence estimator has strong robustness properties with the little loss in asymptotic efficiency relative to the maximum likelihood estimator under model conditions. However, a limitation in applying this estimation method is the algebraic difficulty on an integral involved in an estimation function. This paper considers a minimum density power divergence estimation method with approximated divergence avoiding such difficulty. As an example, we consider the normal-exponential convolution model introduced by Bolstad (2004). The estimated divergence in this case is too complicated; consequently, a Laplace approximation is employed to obtain a manageable form. Simulations and an empirical study show that the minimum density power divergence estimators based on an approximated estimated divergence for the normal-exponential model perform adequately in terms of bias and efficiency.

공간통계분석에서 이상점 수정을 위한 방법비교

  • Lee, Jin-Hui;Sin, Gi-Il
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.05a
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    • pp.275-280
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    • 2003
  • 공간 자료에서 이상점이 존재할 경우 변이도(Variogram)를 추정함에 있어 그 효과를 줄이기 위한 방법으로 로버스트(robust) 변이도를 이용한다. 그러나 이상점이 존재하는 자료분석에서 로버스트 변이도를 사용하기에 앞서 이상점을 수정한 자료를 사용하였을 경우 그 효율성 또한 좋다고 알려져 있다. 본 논문에서는 이상점이 존재하는 자료를 분석함에 있어 기존의 이상점 수정법 및 새로운 이상점 수정법의 효율성을 비교하였다.

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A comparison study of various robust regression estimators using simulation (시뮬레이션을 통한 다양한 로버스트 회귀추정량의 비교 연구)

  • Jang, Soohee;Yoon, Jungyeon;Chun, Heuiju
    • The Korean Journal of Applied Statistics
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    • v.29 no.3
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    • pp.471-485
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    • 2016
  • Least squares (LS) regression is a classic method for regression that is optimal under assumptions of regression and usual observations. However, the presence of unusual data in the LS method leads to seriously distorted estimates. Therefore, various robust estimation methods are proposed to circumvent the limitations of traditional LS regression. Among these, there are M-estimators based on maximum likelihood estimation (MLE), L-estimators based on linear combinations of order statistics and R-estimators based on a linear combinations of the ordered residuals. In this paper, robust regression estimators with high breakdown point and/or with high efficiency are compared under several simulated situations. The paper analyses and compares distributions of estimates as well as relative efficiencies calculated from mean squared errors (MSE) in the simulation study. We conclude that MM-estimators or GR-estimators are a good choice for the real data application.

Robust spectral estimator from M-estimation point of view: application to the Korean housing price index (M-추정에 기반을 둔 로버스트 스펙트럴 추정량: 주택 가격 지수에 대한 응용)

  • Pak, Ro Jin
    • The Korean Journal of Applied Statistics
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    • v.29 no.3
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    • pp.463-470
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    • 2016
  • In analysing a time series on the frequency domain, the spectral estimator (or periodogram) is a very useful statistic to identify the periods of a time series. However, the spectral estimator is very sensitive in nature to outliers, so that the spectral estimator in terms of M-estimation has been studied by some researchers. Pak (2001) proposed an empirical method to choose a tuning parameter for the Huber's M-estimating function. In this article, we try to implement Pak's estimation proposal in the spectral estimator. We use the Korean housing price index as an example data set for comparing various M-estimating results.

A robust test for the parallelism of two regression lines (두 회귀직선의 평행성에 대한 로버스트 검정)

  • 남호수;송문섭;신봉섭
    • The Korean Journal of Applied Statistics
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    • v.8 no.2
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    • pp.77-86
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    • 1995
  • For the problem of testing the parallelism of two regression lines, a robust procedure is proposed and examined. The proposed test statistic is based on the one-step GM-estimators of slope parameters proposed by Song et al. (1994b). These GM-estimators used the Least Trimmed Squares estimates as an initial values so as to obtain high breakdown point. Through a small-sample Monte Carlo simulation the empirical levels and powers of the proposed test are compared with other tests under various error distributions.

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Outlier Detection of Autoregressive Models Using Robust Regression Estimators (로버스트 추정법을 이용한 자기상관회귀모형에서의 특이치 검출)

  • Lee Dong-Hee;Park You-Sung;Kim Kee-Whan
    • The Korean Journal of Applied Statistics
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    • v.19 no.2
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    • pp.305-317
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    • 2006
  • Outliers adversely affect model identification, parameter estimation, and forecast in time series data. In particular, when outliers consist of a patch of additive outliers, the current outlier detection procedures suffer from the masking and swamping effects which make them inefficient. In this paper, we propose new outlier detection procedure based on high breakdown estimators, called as the dual robust filtering. Empirical and simulation studies in the autoregressive model with orders p show that the proposed procedure is effective.

Principal Components Logistic Regression based on Robust Estimation (로버스트추정에 바탕을 둔 주성분로지스틱회귀)

  • Kim, Bu-Yong;Kahng, Myung-Wook;Jang, Hea-Won
    • The Korean Journal of Applied Statistics
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    • v.22 no.3
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    • pp.531-539
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    • 2009
  • Logistic regression is widely used as a datamining technique for the customer relationship management. The maximum likelihood estimator has highly inflated variance when multicollinearity exists among the regressors, and it is not robust against outliers. Thus we propose the robust principal components logistic regression to deal with both multicollinearity and outlier problem. A procedure is suggested for the selection of principal components, which is based on the condition index. When a condition index is larger than the cutoff value obtained from the model constructed on the basis of the conjoint analysis, the corresponding principal component is removed from the logistic model. In addition, we employ an algorithm for the robust estimation, which strives to dampen the effect of outliers by applying the appropriate weights and factors to the leverage points and vertical outliers identified by the V-mask type criterion. The Monte Carlo simulation results indicate that the proposed procedure yields higher rate of correct classification than the existing method.

On the Efficiency of Outlier Cleaners in Spatial Data Analysis (공간통계분석에서 이상점 수정방법의 효율성비교)

  • 이진희;신기일
    • The Korean Journal of Applied Statistics
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    • v.17 no.2
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    • pp.327-336
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    • 2004
  • Many researchers have used the robust variogram to reduce the effect of outliers in spatial data analysis. Recently it is known that estimating the variogram after replacing outliers is more efficient. In this paper, we suggest a new data cleaner for geostatistic data analysis and compare the efficiency of outlier cleaners.