• Journal of Korea Water Resources Association
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• v.32 no.4
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• pp.437-445
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• 1999

The present paper describes the problem of hydrologic response estimation using non-parametric ridge regression method. The method adapted in this work is based on the minimization of the $C_L$ statistics, which is an estimate of the mean square prediction error. For this method, effects of using both the identity matrix and the Laplacian matrix were considered. In addition, we evaluated methods for estimating the error variance of the impulse response. As a result of analyzing synthetic and real data, a good estimation was made when the Laplacian matrix for the weighting matrix and the bias corrected estimate for the error variance were used. The method and procedure presented in present paper will play a robust and effective role on separating hydrologic response.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.3
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• pp.19-29
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• 2018

This research examined the alternatives of Jensen's alpha (α) estimation models in the Capital Asset Pricing Model, discussed by Treynor (1961), Sharpe (1964), and Lintner (1965), using the robust maximum likelihood type m-estimator (MM estimator) and Bayes estimator with conjugate prior. According to finance literature and practices, alpha has often been estimated using ordinary least square (OLS) regression method and monthly return data set. A sample of 50 securities is randomly selected from the list of the S&P 500 index. Their daily and monthly returns were collected over a period of the last five years. This research showed that the robust MM estimator performed well better than the OLS and Bayes estimators in terms of efficiency. The Bayes estimator did not perform better than the OLS estimator as expected. Interestingly, we also found that daily return data set would give more accurate alpha estimation than monthly return data set in all three MM, OLS, and Bayes estimators. We also proposed an alternative market efficiency test with the hypothesis testing Ho: α = 0 and was able to prove the S&P 500 index is efficient, but not perfect. More important, those findings above are checked with and validated by Jackknife resampling results.

• Geomechanics and Engineering
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• v.30 no.1
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• pp.11-26
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• 2022

One of the most important issues in tunneling, is the squeezing phenomenon. Squeezing can occur during excavation or after the construction of tunnels, which in both cases could lead to significant damages. Therefore, it is important to predict the squeezing and consider it in the early design stage of tunnel construction. Different empirical, semi-empirical and theoretical-analytical methods have been presented to determine the squeezing. Therefore, it is necessary to examine the ability of each of these methods and identify the best method among them. In this study, squeezing in a part of the Alborz service tunnel in Iran was estimated through a number of empirical, semi- empirical and theoretical-analytical methods. Among these methods, the most robust model was used to obtain a database including 300 data for training and 33 data for testing in order to develop a machine learning (ML) method. To this end, three ML models of Gaussian process regression (GPR), artificial neural network (ANN) and support vector regression (SVR) were trained and tested to propose a robust model to predict the squeezing phenomenon. A comparative analysis between the conventional and the ML methods utilized in this study showed that, the GPR model is the most robust model in the prediction of squeezing phenomenon. The sensitivity analysis of the input parameters using the mutual information test (MIT) method showed that, the most sensitive parameter on the squeezing phenomenon is the tangential strain (ε_θ^α) parameter with a sensitivity score of 2.18. Finally, the GPR model was recommended to predict the squeezing phenomenon in tunneling projects. This work's significance is that it can provide a good estimation of the squeezing phenomenon in tunneling projects, based on which geotechnical engineers can take the necessary actions to deal with it in the pre-construction designs.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.2
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• pp.70-78
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• 2005

In this paper, a novel rotor flux estimation method of an induction motor using support vector regression(SVR) is presented. Two well-known different flux models with respect to voltage and current are necessary to estimate the rotor flux of an induction motor. Training of SVR which the theory of the SVR algorithm leads to a quadratic programming(QP) problem. The proposed SVR rotor flux estimator guarantees the improvement of performance in the transient and steady state in spite of parameter variation circumstance. The validity and the usefulness of proposed algorithm are throughly verified through numerical simulation.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.541-550
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• 2010

The $L_1$-regression estimator is susceptible to the leverage points, even though it is highly robust to the vertical outliers. This article is concerned with the improvement of robustness of the $L_1$-estimator. To improve its robustness, in terms of the breakdown point, we attempt to dampen the influence of the leverage points by means of reducing the weights corresponding to the leverage points. In addition the algorithm employs the linear scaling transformation technique, for higher computational efficiency with the large data sets, to solve the linear programming problem of $L_1$-estimation. Monte Carlo simulation results indicate that the proposed algorithm yields $L_1$-estimates which are robust to the leverage points as well as the vertical outliers.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.595-611
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• 2016

We use nonlinear regression models (such as the Hill Model) when we analyze data in toxicology and/or pharmacology. In nonlinear regression models an estimator of parameters and estimation of measurement about uncertainty of the estimator are influenced by the variance structure of the error. Thus, estimation methods should be different depending on whether the data are homoscedastic or heteroscedastic. However, we do not know the variance structure of the error until we actually analyze the data. Therefore, developing estimation methods robust to the variance structure of the error is an important problem. In this paper we propose a method to estimate parameters in nonlinear regression models based on a preliminary test. We define an estimator which uses either the ordinary least square estimation method or the iterative weighted least square estimation method according to the results of a simple preliminary test for the equality of the error variance. The performance of the proposed estimator is compared to those of existing estimators by simulation studies. We also compare estimation methods using real data obtained from the National Toxicology program of the United States.

• Geomechanics and Engineering
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• v.9 no.1
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• pp.101-113
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• 2015

Saturated soil hydraulic conductivity is a very important soil parameter in numerous practical engineering applications, especially rainfall infiltration and slope stability problems. This parameter is difficult to measure since it is very highly sensitive to various soil conditions. There have been many analytical and empirical formulas to predict saturated soil hydraulic conductivity based on experimental data. However, there have been few studies to investigate in-situ hydraulic conductivity of weathered granite soils, which constitute the majority of soil slopes in Korea. This paper introduces an estimation method to derive saturated hydraulic conductivity of Korean weathered granite soils using in-situ experimental data which were obtained from a variety of slope areas of South Korea. A robust regression analysis was performed using different physical soil properties and an empirical solution with an $R^2$ value of 0.9193 was suggested. Besides that this research validated the proposed model by conducting in-situ saturated soil hydraulic conductivity tests in two slope areas.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.265-277
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• 1999

Classical and inverse estimation methods are two well known methods in statistical calibration problems. When there are outliers, both methods have large MSE's and could not estimate the input value correctly. We suggest median calibration estimation based on the LD-statistics. To investigate the robust performances, the influence function of the median calibration estimator is calculated and compared with other methods. When there are outliers in the response variables, the influence function is found to be bounded. In simulation studies, the MSE's for each calibration methods are compared. The estimated inputs as well as the performance of the influence functions are calculated.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.97-113
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• 2017

This paper proposes a method for sufficient dimension reduction (SDR) of mixture data. We consider mixture data containing more than one component that have distinct central subspaces. We adopt an approach of a model-based sliced inverse regression (MSIR) to the mixture data in a simple and intuitive manner. We employed mixture probabilistic principal component analysis (MPPCA) to estimate each central subspaces and cluster the data points. The results from simulation studies and a real data set show that our method is satisfactory to catch appropriate central spaces and is also robust regardless of the number of slices chosen. Discussions about root selection, estimation accuracy, and classification with initial value issues of MPPCA and its related simulation results are also provided.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.4
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• pp.84-94
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• 2023

The wave height and period regression curve is widely used to estimate the design wave period. In this study, the parameters of the curves are estimated, compared, and evaluated using the linear, robust linear, and nonlinear regression methods, respectively. The data used in the design wave height estimation are the annual maxima (AM) wave height and period data sets divided by typhoon and non-typhoon conditions, provided by the Ministry of Oceans and Fisheries (2019). The estimation parameters show significant differences in the local coastal waters and the estimation methods. The estimation parameters based on the Suh et al. (2008, 2010) method show the apparent bias, under-estimation in the intercept (scale) parameter, and over-estimation in the slope (exponent) parameter, respectively.