• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.307-316
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• 2004

Principal Component Regression(PCR) and Partial Least Squares Regression(PLSR) are the two most popular regression techniques in chemometrics. In the field of chemometrics usually the number of regressor variables greatly exceeds the number of observation. So we have to reduce the number of regressors to avoid the identifiability problem. In this paper we compare PCR and PLSR techniques combined with various robust regression methods including regression depth estimation. We compare the efficiency, goodness-of-fit and robustness of each estimators under several contamination schemes.

• Communications for Statistical Applications and Methods
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• 제31권3호
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• pp.291-308
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• 2024

The current article discusses ratio type exponential estimators for estimating the mean of a finite population in sample surveys. The estimators uses robust regression's Huber M-estimation function, and their bias as well as mean squared error expressions are derived. It was campared with Kadilar, Candan, and Cingi (Hacet J Math Stat, 36, 181-188, 2007) estimators. The circumstances under which the suggested estimators perform better than competing estimators are discussed. Five different population datasets with a well recognized outlier have been widely used in numerical and simulation-based research. These thorough studies seek to provide strong proof to back up our claims by carefully assessing and validating the theoretical results reported in our study. The estimators that have been proposed are intended to significantly improve both the efficiency and accuracy of estimating the mean of a finite population. As a result, the results that are obtained from statistical analyses will be more reliable and precise.

• 로봇학회논문지
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• 제17권3호
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• pp.296-302
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• 2022

This paper proposes a center of plantar foot pressure (CoP) trajectory estimation method based on Gaussian process regression, with the aim to show robust results regardless of the regions and numbers of FSRs of the insole sensor. This method can bring an interpolation between the measurement points inside the wearable insole sensor, and two experiments are conducted for performance evaluation. For this purpose, the input data used in the experiment are generated in three types (13 FSRs, 8 FSRs, 5 FSRs) according to the regions and numbers of FSRs. First, the estimation results of the CoP trajectory are compared using Gaussian process regression and weighted mean. As a result of each method, the estimation results of the two methods were similar in the case of 13 FSRs data. On the other hand, in the case of the 8 and 5 FSRs data, the weighted mean varies depending on the regions and numbers of FSRs, but the estimation results of Gaussian process regression showed similar results in spite of reducing the regions and numbers. Second, the estimation results of the CoP trajectory based on Gaussian process regression during several gait cycles are analyzed. In five gait cycles, the previous cycle and the current estimation results are compared, and it was confirmed that similar trajectories appeared in all. In this way, the method of estimating the CoP trajectory based on Gaussian process regression showed robust results, and stability was confirmed by yielding similar results in several gait cycles.

• 응용통계연구
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• 제22권3호
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• pp.531-539
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• 2009

로지스틱회귀분석은 고객관계관리를 위한 데이터마이닝 분야에서 많이 사용되는 기법인데, 이 분야의 모형설정 과정에서는 연관성이 매우 높은 설명변수들이 모형에 함께 포함되어 다중공선성의 문제를 유발하며, 더욱이 회귀자료에 이상점들이 포함되면 최우추정량은 심각한 결함을 갖게 된다. 두 가지 문제점을 동시에 해결하기 위하여 로버스트주성분로지스틱회귀를 적용할 수 있는데, 본 논문에서는 주성분의 선정기준을 결정하는 모형을 개발하고, 주성분모형에서의 추정치에 미치는 이상점의 영향을 축소하기 위한 로버스트추정법을 제안하였다. 제안된 추정법은 다중공선성과 이상점이 유발하는 문제들을 적절히 해결해 준다는 사실이 모의실험을 통하여 확인되었다.

• Communications for Statistical Applications and Methods
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• 제1권1호
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• pp.33-40
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• 1994

The conditional expectation of a random variable in a multivariate normal random vector is a multiple linear regression on its predecessors. Using this fact, the least median of squares estimation method developed in a multiple linear regression is adapted to a multivariate data to identify influential observations. The resulting method clearly detect outliers and it avoids the masking effect.

• Journal of the Korean Statistical Society
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• 제16권2호
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• pp.80-91
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• 1987

Minimum $L_i$ norm estimation is a robust procedure ins the sense that it leads to an estimator which has greater statistical eficiency than the least squares estimator in the presence of outliers. And the $L_1$ norm estimator has some desirable statistical properties. In this paper a new computational procedure for $L_1$ norm estimation is proposed which combines the idea of reweighted least squares method and the linear programming approach. A modification of the projective transformation method is employed to solve the linear programming problem instead of the simplex method. It is proved that the proposed algorithm terminates in a finite number of iterations.

• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• 지구물리
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• 제5권2호
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• pp.131-142
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• 2002

일반적으로 지자기 전달함수는 관측치와 예측치의 차이를 최소화하는 관점에서 해가 추정된다. 오차의 구조가 가우스 분포를 따르면 최소자승 추정이 최적의 추정이지만, 그렇지 않은 경우 전달 함수 추정을 심각하게 왜곡시킬 수 있으므로 오차 구조에 대한 정보가 요구된다. 본 연구에서는 Q-Q plot을 이용한 오차 구조으 검증을 통하여 실제 오차 구조에 대한 정보를 획득하였고 가우스 분포 가정을 벗어나는 오차 구조에 대해 외치(outlier)에 의한 영향을 최소로 하며 해를 추정하는 로버스트 추정(regression M-estimate)을 적용하였다. 오차가 가우스 분포를 따르는 경우, 최소자승 추정과 로버스트 추정은 유사한 결과를 나타내나, 오차가 가우스 분포를 벗어나는 경우 로버스트 추정이 최소자승 추정보다 부드러운 결과를 나타냄을 확인하였다.

• Journal of Information Processing Systems
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• 제8권4호
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• pp.621-652
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• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• 제어로봇시스템학회논문지
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• 제18권4호
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• pp.314-320
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• 2012

This paper proposes a new robust motion deblurring filter using the inertial sensor measurements for strapdown image IR applications. With taking the PSF measurement error into account, the motion blurred image is modeled by the linear uncertain state space equation with the noise corrupted measurement matrix and the stochastic parameter uncertainty. This motivates us to solve the motion deblurring problem based on the recently developed robust least squares estimation theory. In order to suppress the ringing effect on the deblurred image, the robust least squares estimator is slightly modified by adoping the ridge-regression concept. Through the computer simulations using the actual IR scenes, it is demonstrated that the proposed algorithm shows superior and reliable motion deblurring performance even in the presence of time-varying motion artifact.