• Communications for Statistical Applications and Methods
• /
• 제27권6호
• /
• pp.701-714
• /
• 2020

Quantitative high throughput screening (qHTS) assays are used to assess toxicity for many chemicals in a short period by collectively analyzing them at several concentrations. Data are routinely analyzed using nonlinear regression models; however, we propose a new method to analyze qHTS data using a nonlinear mixed effects model. qHTS data are generated by repeating the same experiment several times for each chemical; therefor, they can be viewed as if they are repeated measures data and hence analyzed using a nonlinear mixed effects model which accounts for both intra- and inter-individual variabilities. Furthermore, we apply a one-step approach incorporating robust estimation methods to estimate fixed effect parameters and the variance-covariance structure since outliers or influential observations are not uncommon in qHTS data. The toxicity of chemicals from a qHTS assay is classified based on the significance of a parameter related to the efficacy of the chemicals using the proposed method. We evaluate the performance of the proposed method in terms of power and false discovery rate using simulation studies comparing with one existing method. The proposed method is illustrated using a dataset obtained from the National Toxicology Program.

• 한국기계가공학회지
• /
• 제7권3호
• /
• pp.3-11
• /
• 2008

Robust design pioneered by Dr. G. Taguchi has been applied to versatile engineering problems for improving quality. Since 1980s, the Taguchi method has been introduced to numerical optimization, complementing the deficiencies of deterministic optimization, which is often called the robust optimization. In this study, the robust optimization strategy is proposed by considering the robustness of objective and constraint functions. The statistics of responses in the functions are surrogated by kriging models. In addition, objective and/or constraint function is represented by the probability of success, thus facilitating robust optimization. The mathematical problem and the two-bar design problem are investigated to show the validity of the proposed method.

• Communications for Statistical Applications and Methods
• /
• 제8권3호
• /
• pp.625-632
• /
• 2001

Principal component analysis(PCA) is a multivariate technique falling under the general title of factor analysis. The purpose of PCA is to Identify the dependence structure behind a multivariate stochastic observation In order to obtain a compact description of it. In engineering field PCA is utilized mainly (or data compression and restoration. In this paper we propose a new robust Hebbian algorithm for robust PCA. This algorithm is based on a hyperbolic tangent function due to Hampel ef al.(1989) which is known to be robust in Statistics. We do two experiments to investigate the performance of the new robust Hebbian learning algorithm for robust PCA.

• Communications for Statistical Applications and Methods
• /
• 제2권2호
• /
• pp.63-73
• /
• 1995

We consider some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean. The proposed estimators are compared with the sample mean and subjective Bayes estimators in terms of "posterior robustness" and "procedure robustness".re robustness".uot;.

• 응용통계연구
• /
• 제25권2호
• /
• pp.331-339
• /
• 2012

Statistical matching is a methodology used to merge microdata from two (or more) files into a single matched file, the variants of which have been extensively studied. Among existing studies, we focused on Moriarity and Scheuren's (2001) method, which is a representative method of statistical matching for continuous data. We examined this method and proposed a revision to it by using a robust approach in the regression step of the procedure. We evaluated the efficiency of our revised method through simulation studies using both simulated and real data, which showed that the proposed method has distinct advantages over existing alternatives.

• Communications for Statistical Applications and Methods
• /
• 제7권1호
• /
• pp.225-232
• /
• 2000

The linear and quadratic discrimination functions based on normal theory are widely used to classify an observation to one of predefined groups. But the discriminant functions are sensitive to outliers. A high breakdown procedure to estimate location and scatter of multivariate data is the minimum volume ellipsoid or MVE estimator To obtain high breakdown classifiers outliers in multivariate data are detected by using the robust Mahalanobis distance based on MVE estimators and the weighted estimators are inserted in the functions for classification. A samll-sample MOnte Carlo study shows that the high breakdown robust procedures perform better than the classical classifiers.

• Communications for Statistical Applications and Methods
• /
• 제27권4호
• /
• pp.459-468
• /
• 2020

We examine the rate of substitution of jobs by artificial intelligence using a score called the "weighted ability rate of substitution (WARS)." WARS is a indicator that represents each job's potential for substitution by automation and digitalization. Since the conventional WARS is sensitive to the particular responses from the employees, we consider a robust version of the indicator. In this paper, we propose the individualized WARS, which is a modification of the conventional WARS, and compute robust averages and confidence intervals for inference. In addition, we use the clustering method to statistically classify jobs according to the proposed individualized WARS. The proposed method is applied to Korean job data, and proposed WARS are computed for five future years. Also, we observe that 747 jobs are well-clustered according to the substitution levels.

• Journal of the Korean Statistical Society
• /
• 제27권1호
• /
• pp.113-132
• /
• 1998

In this paper, we consider a minimum distance (M.D.) estimation based on kernels for U-statistics. We use Cramer-von Mises type distance function which measures the discrepancy between U-empirical distribution function(d.f.) and modeled d.f. of kernel. In the distance function, we allow various integrating measures, which can be finite, $\sigma$-finite or discrete. Then we derive the asymptotic normality and study the qualitative robustness of M. D. estimates.

• 응용통계연구
• /
• 제31권4호
• /
• pp.463-473
• /
• 2018

연구에서는 선형회귀모형을 가정한 대형 데이터에서의 변수선택 알고리즘을 다룬다. 방법의 속도와 강건성에 주안점을 둔 여러 알고리즘들이 제안되었다. 그 중에서 streamwise 회귀 접근법을 사용한 VIF회귀는 신속하고 정확하게 수행된다. 그러나 VIF회귀는 최소제곱방법에 의해 모형이 추정되므로 이상치에 민감하다. 변수선택방법의 강건성을 높이기 위해 가중 추정치를 사용한 강건측도가 제안되었으며 강건 VIF회귀도 제안되었다. 본 연구에서는 잠재적 이상치를 탐지하여 제거한 후 VIF회귀를 수행하는, 빠르고 강건한 변수선택 방법을 제안한다. 제안된 방법은 모의실험과 데이터 분석 통해 다른 방법들과 비교된다.

• Journal of the Korean Data and Information Science Society
• /
• 제11권1호
• /
• pp.65-72
• /
• 2000

로버스트설계는 품질공학에서 품질특성치의 수행변동(performance variation)을 줄이는데 있다. 다구찌가 제안한 파라미터설계는 아주 많은 장점을 가지고 있으나 몇 가지 단점이 있다. 그 중에서 파라미터설계에 있어서 교차배열은 제어인자와 잡음인자의 모든 교호작용효과를 고려한 실험배치이기 때문에 많은 실험횟수를 필요로 하는 단점이 있다. 그래서 대안방법으로 Welch등(1990)이 제안한 통합배열이 고려된다. 본 논문에서는 로버스트 설계를 위한 다구찌의 파라미터설계(혹은 교차배열방법론)와 통합배열방법론을 시뮬레이션을 통하여 비교 연구하고자 한다.