• Journal of the Korean Data and Information Science Society
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• 제26권3호
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• pp.549-559
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• 2015

MDPDE는 이상치에 강건한 성질을 가진 추정량으로써 최대우도추정량의 대안으로 많은 연구자들에 의해 연구되어 왔다. MDPDE는 조율모수에 따라 성질이 변하게 되는데, 로버스트성과 점근효율성이 서로 상충하는 현상으로 인해 최적의 조율모수를 선택하는 것은 쉽지 않다. 본 연구에서는 MDPDE의 최적의 조율모수를 선택하는 방법으로 Fujisawa와 Eguchi (2006)가 제시한 방법과 Warwick (2006)이 제시한 방법을 소개하고, 모의실험을 통해 비교분석하였다. 연구 결과 Warwick (2006)의 방법은 특정한 경우 매우 작은 조율모수를 선택하게 될 수도 있다는 사실을 알 수 있었는데, 같은 경우에 Fujisawa와 Eguchi (2006)의 방법은 이러한 현상을 보이지 않았다. 따라서, Fujisawa와 Eguchi (2006)의 방법이 범용적으로 사용하기에 적절하다고 판단된다.

• Quantitative Bio-Science
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• 제37권2호
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• pp.133-141
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• 2018

Graphical lasso is one of the most popular methods to estimate a sparse precision matrix, which is an inverse of a covariance matrix. The objective function of graphical lasso imposes an ${\ell}_1$-penalty on the (vectorized) precision matrix, where a tuning parameter controls the strength of the penalization. The selection of the tuning parameter is practically and theoretically important since the performance of the estimation depends on an appropriate choice of tuning parameter. While information criteria (e.g. AIC, BIC, or extended BIC) have been widely used, they require an asymptotically unbiased estimator to select optimal tuning parameter. Thus, the biasedness of the ${\ell}_1$-regularized estimate in the graphical lasso may lead to a suboptimal tuning. In this paper, we propose a two-staged bias-correction procedure for the graphical lasso, where the first stage runs the usual graphical lasso and the second stage reruns the procedure with an additional constraint that zero estimates at the first stage remain zero. Our simulation and real data example show that the proposed bias correction improved on both edge recovery and estimation error compared to the single-staged graphical lasso.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.673-683
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• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

• 전기학회논문지
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• 제63권7호
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• pp.883-888
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• 2014

The paper describes the parameter tuning of power system stabilizer (PSS) for a power plant based on hybrid system modeling. The existing tuning method based on bode plot and root locus is well applied to keep power system stable. However, due to linearization of power system and an assumption that the parameter ratio of the lead-lag compensator in PSS is fixed, the results cannot guarantee the optimal performances to damp out low-frequency oscillation. Therefore, in this paper, hybrid system modeling, which has a DAIS (differential-algebraic-impusive-switched) structure, is applied to conduct nonlinear modeling for power system and find optimal parameter set of the PSS. The performances of the proposed method are carried out by time domain simulation with a single machine connected to infinite bus (SMIB) system.

• 대한기계학회논문집A
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• 제24권5호
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• pp.1271-1280
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• 2000

A sensor placement technique to identify structural parameter was developed. Experimental results must be acquired to identify unknown dynamic characteristics of a targeting structure for the comparison between analytical model and real structure. If the experimental environment was not equipped itself properly, it can be happened that some valuable information are distorted or ill-condition can be occurred. In this work the index to determine exciting points was derived from the criterion of maximizing parameter sensitivity matrix and that to choose measurement points was from that of preserving the invariant of sensitivity matrix. This idea was applied to a compressor hull structure to verify its performance. The result shows that the selection of measurement and excitation points using suggested criteria improve the ill-conditioning problem of inverse type problems such , as model updating.

• Journal of the Korean Statistical Society
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• 제30권3호
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• pp.453-465
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• 2001

It is often the case that one wants to estimate parameters of the distribution which follows certain parametric model, while the dta are contaminated. it is well known that the maximum likelihood estimators are not robust to contamination. Basuet al.(1998) proposed a robust method called the minimum density power divergence estimation. In this paper, we investigate data-driven selection of the tuning parameter $\alpha$ in the minimum density power divergence estimation. A criterion is proposed and its performance is studied through the simulation. The simulation includes three cases of estimation problem.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• 응용통계연구
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• 제29권4호
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• pp.643-656
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• 2016

고차원 자료(high dimensional data)는 변수의 수가 표본의 수보다 많은 자료로 다양한 분야에서 관측 또는 생성되고 있다. 일반적으로, 고차원 자료에 대한 회귀 모형에서는 모수의 추정과 과적합을 피하기 위하여 변수 선택이 이루어진다. 벌점화 회귀 모형(penalized regression model)은 변수 선택과 회귀 계수의 추정을 동시에 수행하는 장점으로 인하여 고차원 자료에 빈번하게 적용되고 있다. 하지만, 벌점화 회귀 모형에서도 여전히 조율 모수 선택(tuning parameter selection)을 통한 최적의 모형 선택이 요구된다. 본 논문에서는 벌점화 회귀 모형 중에서 대표적인 LASSO 회귀 모형을 기반으로 모형 선택의 기준들에 대한 LASSO 회귀 추정량의 편의가 어떠한 영향을 미치는지 모의실험을 통하여 수치적으로 연구하였고 편의의 보정의 필요성에 대하여 나타내었다. 실제 자료 분석에서의 영향을 나타내기 위하여, 폐암 환자의 유전자 발현량(gene expression) 자료를 기반으로 바이오마커 식별(biomarker identification) 문제에 적용하였다.

• 조명전기설비학회논문지
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• 제23권2호
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• pp.175-181
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• 2009

본 논문에서는 전력계통의 안정도를 향상시키기 위하여 동기 발전기와 정지형 무효전력 보상기에 대한 파라미터 자기조정 퍼지제어기의 설계 기법을 제시한다. 제안된 퍼지제어기의 파라미터 자기조정 알고리즘은 퍼지제어기의 추론값과 전력계통안정화 장치의 출력값들 사이의 오차를 감소시키는 두 개의 방향 벡터를 사용하는 최급강하법에 기초를 둔다. 전력계통안정화 장치로부터 얻어진 입 출력 데이터쌍을 사용하여, 퍼지추론 규칙의 전건부와 후건부에서의 파라미터들은 제안된 최급강하법에 의해 자동조정되고 학습되어진다. 시뮬레이션 결과, 제안된 퍼지제어기가 종래의 제어기보다 우수한 제어성능을 보임을 확인하였다.

• 제어로봇시스템학회논문지
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• 제7권12호
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• pp.973-979
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• 2001

Sliding mode control guarantees robustness in the presence of modeling uncertainties and external disturbances. However, this can be obtained at the cost of high control activity that may lead to chattering As one way to alleviate this problem a boundary layer around sliding surface is typically used. In this case the selection of controller gain, control ban width and boundary layer thickness is a crucial problem for the trade-off between tracking error and chattering. The parameter tuning is usually done by trail-and-error in practice causing significant effort and time. An auto tuning method based on fuzzy rules is proposed in the paper in this method tracking error and chattering are monitored by performance indices and the controller tunes the design parameters intelligently in order to compromise both indices. To demonstrate the efficiency of the propose method a mass-spring translation system and a roboic control system are simulated and tested It is shown that the proposed algorithm is effective to facilitae the parameter tuning for sliding mode controllers.