• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.327-332
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• 1997

다층 신경망은 비모수 회귀함수 추정의 한 방법이다. 다충 신경망을 학습시키기 위해 역전파 알고리즘이 널리 사용되고 있다. 그러나 이 알고리즘은 이상치에 매우 민감하여 이상치를 포함하고 있는 자료에 대하여 원하지 않는 회귀함수를 추정한다. 본 논문에서는 통계물리에서 자주 사용하는 방법을 이용하여 로버스트 역전파 알고리즘을 제안하고 수학적으로 신경망과 매우 유사한 PRP(projection pursuit regression) 방법, 일반적인 역전파 알고리즘과 모의실험을 통해 비교 분석한다.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.725-735
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• 2015

The aim of this study is to specifically understand the officetel market by empirical analysis for the determining factors that affect determining the price of the officetel in Busan. In my opinion, it can help officetel providers to select the appropriate size and location that analysis for the factors determining officetel price with market price, and also it can help customers officetel to choice depending on the purpose. So I was conducting this study. In this study, I analyzes the factors determining the price of Officetel using a OLS linear regression, semi-log model, and a robust regression-Busan area Officetel Real Transaction Price as the dependent variable and factors representing the physical characteristics, locational characteristics and regional characteristics as independent variables.

• 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.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.345-355
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• 1998

The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data are employed. In this paper two types of robust backpropagation algorithms are discussed both from a theoretical point of view and in the case studies of nonlinear regression function estimation and handwritten Korean character recognition. For future research we suggest Bayesian learning approach to neural networks and compare it with two robust backpropagation algorithms.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.889-898
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• 2019

The problem of missing value imputation for variables in surveys that include item missing becomes complicated if outliers and logical boundary conditions between other survey items cannot be ignored. If there are outliers and boundaries in a variable including missing values, imputed values based on previous regression-based imputation methods are likely to be biased and not meet boundary conditions. In this paper, we approach these difficulties in imputation by combining various robust regression models and multiple imputation methods. Through a simulation study on various scenarios of outliers and boundaries, we find and discuss the optimal combination of robust regression and multiple imputation method.

• The Korean Journal of Applied Statistics
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• v.1 no.2
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• pp.10-26
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• 1987

If the forecasts from different, sources are combined in some way, the resulting forecasts may be more accurate than any of the individual components. In this paper, the established procedures of combining forecasts are reviewed and the alternative procedures are suggested. By the results of empirical analysis from survey data, the method of combining forecasts using the restricted regression weights, the restricted robust regression weights, and mixed regression weights are robust. We can not find the most efficient combined forecasts in any case if we select the corresponding decision by preliminary analysis for the statistical properties of individual dorecasts, our results of combined forecast can became useful.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.991-1001
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• 2015

Digital images acquired by digital devices are used in many fields. Applying statistical methods to the processing of images will increase speed and efficiency. Methods to remove noise and image quality have been researched as a basic operation of image processing. This paper proposes a novel reduction method that considers the direction and magnitude of the edge to remove image noise effectively using statistical methods. The proposed method estimates the brightness of pixels relative to pixels in the same direction based on a robust regression model. An estimate of pixel brightness is obtained by weighting the magnitude of the edge that improves the performance of the average filter. As a result of the simulation study, the proposed method retains pixels that are well-characterized and confirms that noise reduction performance is improved over conventional methods.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.163-171
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• 1997

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Multilayer feedforward neural networks have been proposed as a good approximator of nonlinear function. The back propagation(BP) algorithm allows multilayer feedforward neural networks to learn input-output mappings from training samples. It iteratively adjusts the network parameters(weights) to minimize the sum of squared approximation errors using a gradient descent technique. However, the mapping acquired through the BP algorithm may be corrupt when errorneous training data we employed. When errorneous traning data are employed, the learned mapping can oscillate badly between data points. In this paper we propose a robust BP learning algorithm that is resistant to the errorneous data and is capable of rejecting gross errors during the approximation process, that is stable under small noise perturbation and robust against gross errors.