• Wind and Structures
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• v.26 no.6
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• pp.383-395
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• 2018

An accurate determination of wind speed distribution is the basis for an evaluation of the wind energy potential required to design a wind turbine, so it is important to estimate unknown parameters of wind speed distribution. In this paper, Gumbel distribution is used in modelling wind speed data, and alternative robust estimation methods to estimate its parameters are considered. The methodologies used to obtain the estimators of the parameters are least absolute deviation, weighted least absolute deviation, median/MAD and least median of squares. The performances of the estimators are compared with traditional estimation methods (i.e., maximum likelihood and least squares) according to bias, mean square deviation and total mean square deviation criteria using a Monte-Carlo simulation study for the data with and without outliers. The simulation results show that least median of squares and median/MAD estimators are more efficient than others for data with outliers in many cases. However, median/MAD estimator is not consistent for location parameter of Gumbel distribution in all cases. In real data application, it is firstly demonstrated that Gumbel distribution fits the daily mean wind speed data well and is also better one to model the data than Weibull distribution with respect to the root mean square error and coefficient of determination criteria. Next, the wind data modified by outliers is analysed to show the performance of the proposed estimators by using numerical and graphical methods.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1653-1660
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• 2016

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.

• 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.

• Journal of the Korean Statistical Society
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• v.33 no.3
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• pp.323-337
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• 2004

This article coined a general class of estimators for various measures in normal population when some' a priori' or guessed value of standard deviation a is available in addition to sample information. The class of estimators is primarily defined for a function of standard deviation. An unbiased estimator and the minimum mean squared error estimator are worked out and the suggested class of estimators is compared with these classical estimators. Numerical computations in terms of percent relative efficiency and absolute relative bias established the merits of the proposed class of estimators especially for small samples. Simulation study confirms the excellence of the proposed class of estimators. The beauty of this article lies in estimation of various measures like standard deviation, variance, Fisher information, precision of sample mean, process capability index $C_{p}$, fourth moment about mean, mean deviation about mean etc. as particular cases of the proposed class of estimators.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.655-662
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• 2012

Classification is an important research field in pattern recognition with high-dimensional predictors. The support vector machine(SVM) is a penalized feature selector and classifier. It is based on the hinge loss function, the non-convex penalty function, and the smoothly clipped absolute deviation(SCAD) suggested by Fan and Li (2001). We developed the algorithm for the multiclass SVM with the SCAD penalty function using the local quadratic approximation. For multiclass problems we compared the performance of the SVM with the $L_1$, $L_2$ penalty functions and the developed method.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.101-112
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• 1998

This paper is concerned with the asymptotic properties of the least absolute deviation estimators for the nonlinear regression model when dependent variables are subject to censoring time, and proposed the simple and practical sufficient conditions for the strong consistency and asymptotic normality of the least absolute deviation estimators in censored regression model. Some desirable asymptotic properties including the asymptotic relative efficiency of proposed model with respect to standard model are given.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.14 no.24
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• pp.111-121
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• 1991

This paper considers a non preemtive single processor scheduling problem in which each set have the two common due dates. The objective of the problem is to minimize the weighted mean absolute deviation of job completion times about such two common due dates under the assumption that each job has a different weight. Such a job sequence is an W-shaped sequence. We propose three heuristic solution methods based on several dominance conditions. Numerical examples are presented. The performance comparison is made among three heuristic scheduling procedures.

• Journal of the Korea Society for Simulation
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• v.3 no.1
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• pp.17-28
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• 1994

A statistical procedure is developed to estimate the relative difference between two parameters each obtained from either true model or approximate model. Double sample procedure is applied to find the additional number of simulation runs satisfying the preassigned absolute precision of the confidence interval. Two types of parameters, mean and standard deviation, are considered as the performance measures and tried to show the validity of the model by examining both queues and inventory systems. In each system it is assumed that there are three distinct means and their own standard deviations and they form the simultaneous confidence intervals but with control in the sense that the absolute precision for each confidence interval is bounded on the limits with preassigned confidence level. The results of this study may contribute to some situations, for instance, first, we need a statistical method to compare the effectiveness between two alternatives, second, we find the adquate number of replications with any level of absolute precision to avoid the unrealistic cost of running simulation models, third, we are interested in analyzing the standard deviation of the output measure, ..., etc.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.12
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• pp.1381-1388
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• 2014

This paper is proposed a wavelet-based the order statistics MAD(Median Absolute Deviation) method of SAR(Synthetic Aperture Radar) image for image enhancement. also The method of compared and defined the threshold the wavelet coefficients using MAD of the wavelet coefficients of the detail subbands was proposed to effectively image enhancement. In order to complement the disadvantage, the threshold of the proposed method sets up the image statistic and excludes the distortion. The hardware design is used FPGA of Xilinx and DSP system for the image enhancement and compressed encoding of the proposed algorithm. Therefore the proposed method is totally verified by comparing with the several other images.

• Journal of the Korean Society of Safety
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• v.22 no.5
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• pp.84-89
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• 2007

The lower explosion limit(LEL) is one of the major combustion properties used to determine the fire and explosion hazards of the combustible substances. In this study, the lower explosion limits of the ester compounds were predicted by using the normal boiling points and the flash points based on the liquid thermodynamic theory. As a results, the A.A.P.E.(average absolute percent error) and the A.A.D.(average absolute deviation) of the reported and the calculated the LEL for the ester are 8.80 vol% and 0.18 vol%, respectively and the coefficient of correlation was 0.965. From a given results, by the use of the proposed methodology, it is possible to predict the lower explosion limits of the other flammable materials.