• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1309-1317
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• 2013

There are various parameter estimation methods for the generalized exponential distribution under progressive type I interval censoring. Chen and Lio (2010) studied the parameter estimation method by the maximum likelihood estimation method, mid-point approximation method, expectation maximization algorithm and methods of moments. Among those, mid-point approximation method has the smallest mean square error in the generalized exponential distribution under progressive type I interval censoring. However, this method is difficult to derive closed form of solution for the parameter estimation using by maximum likelihood estimation method. In this paper, we propose two type of approximate maximum likelihood estimate to solve that problem. The simulation results show the obtained estimators have good performance in the sense of the mean square error. And proposed method derive closed form of solution for the parameter estimation from the generalized exponential distribution under progressive type I interval censoring.

• Journal of the Korean Geophysical Society
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• v.5 no.2
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• pp.131-142
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• 2002

Geomagnetic transfer function is generally estimated by choosing transfer to minimize the square sum of differences between observed values. If the error structure sccords to the Gaussian distribution, standard least square(LS) can be the estimation. However, for non-Gaussian error distribution, the LS estimation can be severely biased and distorted. In this paper, the Gaussian error assumption was tested by Q-Q(Quantile-Quantile) plot which provided information of real error structure. Therefore, robust estimation such as regression M-estimate that does not allow a few bad points to dominate the estimate was applied for error structure with non-Gaussian distribution. The results indicate that the performance of robust estimation is similar to the one of LS estimation for Gaussian error distribution, whereas the robust estimation yields more reliable and smooth transfer function estimates than standard LS for non-Gaussian error distribution.

• Geomechanics and Engineering
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• v.14 no.5
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• pp.453-458
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• 2018

Geomechanical parameters are important factors for engineering projects during design, construction and support stages of tunnel and dam projects. Geostatistical estimation methods are known as one of the most significant approach at estimation of Geomechanical parameters. In this study, Azad dam headrace tunnel is chosen to estimate Geomechanical parameters such as Rock Quality Designation (RQD) and uniaxial compressive strength (UCS) by ordinary kriging as a geostatistical method. Also Rock Mass Rating (RMR) distribution is presented along the tunnel. Main aim in employment of geostatistical methods is estimation of points that unsampled by sampled points.To estimation of parameters, initially data are transformed to Gaussian distribution, next structural data analysis is completed, and then ordinary kriging is applied. At end, specified distribution maps for each parameter are presented. Results from the geostatistical estimation method and actual data have been compared. Results show that, the estimated parameters with this method are very close to the actual parameters. Regarding to the reduction of costs and time consuming, this method can use to geomechanical estimation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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• pp.24-36
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• 1996

In this paper, we discuss the errors in selectivity estimation using the multilevel grid file (MLGF). We first demonstrate that the estimatio errors stem from the uniformity assumption that records are uniformly distributed in their belonging region represented by an entry in a level of an MLGF directory. Bsed on this demonstration, we then investigate five factors affecting the accuracy of estimation: (1) the data distribution in a region (2) the number of records stored in an MLFG (3) the page size, (4) the query region size, and (5) the level of an MLFG directory. Next we present the tendancy of estimation errors according to the change of values for each factor through experiments. The results show that the errors decrease when (1) the distribution of records in a region becomes closer to the uniform one, (2) the number of records in an MLFG increases, (3) the page size decreases, (4) the query region size increases, and (5) the level of an MLFG directory employed as data distribution information becomes lower. After the definition of the granule ratio, the core formula representing the basic relationship between the estimation errors and the above five factors, we finally examine the change of estimation errors according to the change of the values for the granule ratio through experiments. The results indicate that errors tend to be similar depending on the values for the granule ratio regardless of the various changes of the values for the five factors. factors affecting the accuracy of estimation:

• IE interfaces
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• v.21 no.2
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• pp.151-160
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• 2008

Defect size distribution is a probability density function for the defects that occur on wafers or glasses during semiconductor/LCD fabrication. It is one of the most important information to estimate manufacturing yield using well-known statistical estimation methods. The defects are detected by automatic optical inspection (AOI) facilities. However, the data that is provided from AOI is not accurate due to resolution of AOI and its defect detection mechanism. It causes distortion of defect size distribution and results in wrong estimation of the manufacturing yield. In this paper, I suggest a size conversion method and a maximum likelihood estimator to overcome the vague defect size information of AOI. The methods are verified by the Monte Carlo simulation that is constructed as similar as real situation.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.855-859
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• 2007

It is essential to obtain accurate and highly reliable streamflow data for water resources planning, evaluation and management as well as design of hydraulic structures. A new discharge estimation method, which is named 'non-dimensional velocity distribution and index-velocity method,' was proposed in this research. This method showed very close channel discharges which were calculated with the exiting velocity-area method. When velocity-area method is used to estimate channel discharge, it is required to observe point velocities at every desired point and vertical using a current meter like Price-AA. However 'non-dimensional velocity distribution and index-velocity method' is used, it become optional to observe point velocities at every desired point and vertical. But this method can not be applied for the cases of very complex and strongly asymmetric channel cross-sections because non-dimensional velocity distribution by entropy concept may be quite biased from that of natural rivers.

• Journal of Korean Society of Forest Science
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• v.61 no.1
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• pp.1-7
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• 1983

In the estimation of diameter distribution in a stand using Weibull distribution function, the calculation method of experimental distribution was presented in previous paper. This study was to estimate the diameter distribution of Korean pine stands by Weibull distribution which represents Gamma function, with mean diameter and mean basal-area diameter of the random sample trees. The results obtained fitted the diameter distribution in experimental stands. Thus, this method appears to be used for the estimation of diameter distribution in a stand as well as for the analysis and prediction of stand construction for the future.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.101-114
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• 2012

In car insurance, the loss ratio is the ratio of total losses paid out in claims divided by the total earned premiums. In order to minimize the loss to the insurance company, estimating extreme quantiles of loss ratio distribution is necessary because the loss ratio has essential prot and loss information. Like other types of insurance related datasets, the distribution of the loss ratio has heavy-tailed distribution. The Peaks over Threshold(POT) and the Hill estimator are commonly used to estimate extreme quantiles for heavy-tailed distribution. This article compares and analyzes the performances of various kinds of parameter estimating methods by using a simulation and the real loss ratio of car insurance data. In addition, we estimate extreme quantiles using the Hill estimator. As a result, the simulation and the loss ratio data applications demonstrate that the POT method estimates quantiles more accurately than the Hill estimation method in most cases. Moreover, MLE, Zhang, NLS-2 methods show the best performances among the methods of the GPD parameters estimation.

• Journal of Radiation Protection and Research
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• v.31 no.1
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• pp.17-23
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• 2006

The purpose of this study is to provide the guidance necessary for making a selection of error distributions by analyzing influence of statistical distribution for a type of bioassay measurement error on the intake estimation. For this purpose, intakes were estimated using maximum likelihood method for cases that error distributions are normal and lognormal, and comparisons between two distributions for the estimated intakes were made. According to the results of this study, in case that measurement results for lung retention are somewhat greater than the limit of detection it appeared that distribution types have negligible influence on the results. Whereas in case of measurement results for the daily excretion rate, the results obtained from assumption of a lognormal distribution were 10 % higher than those obtained from assumption of a normal distribution. In view of these facts, in case where uncertainty component is governed by counting statistics it is considered that distribution type have no influence on intake estimation. Whereas in case where the others are predominant, it is concluded that it is clearly desirable to estimate the intake assuming a lognormal distribution.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.463-477
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• 2009

Parameter estimation methods such as maximum likelihood estimation method, probability weighted moments method, regression method have been popularly applied to various extreme value models in numerous literature. Among three methods above, the performance of regression method has not been rigorously investigated yet. In this paper the regression method is compared with the other methods via Monte Carlo simulation studies for estimation of parameters of the Generalized Extreme Value(GEV) distribution and the Generalized Pareto(GP) distribution. Our simulation results indicate that the regression method tends to outperform other methods under small samples by providing smaller biases and root mean square errors for estimation of location parameter of the GEV model. For the scale parameter estimation of the GP model under small samples, the regression method tends to report smaller biases than the other methods. The regression method tends to be superior to other methods for the shape parameter estimation of the GEV model and GP model when the shape parameter is -0.4 under small and moderately large samples.