• Journal of the Korean Mathematical Society
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• v.36 no.6
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• pp.1033-1046
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• 1999

This paper is devoted to the point wise error estimate up to boundary for the standard finite element solution of Poisson equation with Dirichlet boundary condition. Our new approach used the discrete maximum principle for the discrete harmonic solution. once the mesh in our domain satisfies the $\beta$-condition defined by us, the discrete harmonic solution with dirichlet boundary condition has the discrete maximum principle and the pointwise error should be bounded by L-errors newly obtained.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.371-383
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• 2019

Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

• Korean Journal of Health Education and Promotion
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• v.19 no.4
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• pp.77-97
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• 2002

Since growth promotion was defined by Koch(1935), many researches like Benholdt and Thomsen(1942) have conducted studies for understanding problem of puberty growth. Growth promotion means that growth is developed in puberty, and several researchers have reported that the more becomes economic growth, the more becomes growth promotion. Thereupon, this study was attempted to find Maximum Growth Age(M.G.A.), as an index of height growth promotion in Korea, which was obtained by longitudinal observations of the same group. Thus, this study can explain the earlier tendency of growth. To investigate domestic changes in M.G.A., M.G.A. was calculated with the results of cross-sectional researchs using 25 representative papers between 1940-1953 including measurements by Lee(1940) and data by Kim(1953) in this study. Based on the research data published between 1940 and 2000, height and M.G.A. of males and females who were born between 1925 and 1983 were gotten by years, and a trend of growth promotion for height in Koreans was suggested by examining study subjects. Findings of this study are as follows; 1. M.G.A. for height decreased both in males and females; for males, 14.28 years in 1940, 14.24 in 1953, 13.86 in 1967, 12.74 in 1985, and 11.71 in 2000; for females, 12.0 in 1940, 11.52 in 1965, 10.00 in 1978 and 9.77 in 2000. 2. Regression equations and standard errors of estimate concerning M.G.A. for height by years were obtained; for males, Y$_1$(M.G.A.) = 17.21 - 0.059X$_1$, S$_{Y1X1}$(standard error of estimate about the regression line) = ${\pm}$0.62; for females, Y$_2$(M.G.A.) = 13.81-0.042X$_2$, S$_{Y2X2}$(standard error of estimate about the regression line) = ${\pm}$0.64 3. As a result of finding correlation between year and M.G.A. r=-0.763 (p<0.001) for male and r=-0.699(p<0.001) for female were obtained 4. From a view that the growth promotion has been continued before 2000, M.G.A. decreased 0.6 years for male and 0.4 for female per 10 years. 5. M.G.A. for height is as shown in Table 2. 6. It is thought that the future trend of growth promotion for height will follow the progress from 1940s to now. It shall be reviewed again after development of coming several years is investigated.

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.397-410
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• 2007

For the two groups data from multivariate binomial distribution, we consider a bootstrap approach to inferring the simultaneous confidence level and its standard error of a collection of the dependent confidence intervals for the difference of proportions with an experimentwise error rate at the a level are presented. The bootstrap method is used to estimate the simultaneous confidence probability for the difference of proportions.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.74-80
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• 2005

A number of creep rupture data for type 316LN stainless steels were collected through literature survey or experimental data produced in KAERI. Using these data, polynomial equations for predicting creep life were obtained by Larson-Miller (L-M), Orr-Sherby-Dorn (O-S-D) and Manson-Haferd (M-H) parameters using time-temperature parametric (TTP) methods. Standard error of estimate (SEE) values for the each parameter was obtained with different temperatures through the statistical process of the creep data. The results of L-M, O-S-D and M-H methods showed good creep-life prediction, but M-H method showed better agreement than L-M and O-S-D methods. Especially, it was found that SEE values of M-H method at $700^{\circ}C$ were lower than that of L-M and O-S-D methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1406-1411
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• 2005

The creep rupture data for type 316LN stainless steels were collected through literature survey or experimental data produced in KAERI. Using these data, polynomial equations for predicting creep life were obtained by Larson-Miller (L-M), Orr-Sherby-Dorn (O-S-D) and Manson-Haferd (M-H) etc. time-temperature parametric (TTP) methods. Standard error of estimate (SEE) values for the each parameter was obtained with different temperatures through the statistical process of the creep data. The results of L-M, O-S-D and M-H methods showed good creep-life prediction, but M-H method showed better agreement than L-M and O-S-D methods. Especially, it was found that SEE values of M-H method at $700^{\circ}C$ were lower than that of L-M and O-S-D methods.

• Korean Journal of Clinical Laboratory Science
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• v.38 no.3
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• pp.184-188
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• 2006

The purpose of the recovery experiment in clinical chemistry is performed to estimate proportional systematic error. We must know all measurements have some error margin in measuring analytical performance. Proportional systematic error is the type of error whose magnitude increases as the concentration of analyte increases. This error is often caused by a substance in the sample matrix that reacts with the sought for analyte and therefore competes with the analytical reagent. Recovery experiments, therefore, are used rather selectively and do not have a high priority when another analytical method is available for comparison purposes. They may still be useful to help understand the nature of any bias revealed in the comparison of kit experiments. Recovery should be expressed as a percentage because the experimental objective is to estimate proportional systematic error, which is a percentage type of error. Good recovery is 100.0%. The difference between 100 and the observed recovery(in percent) is the proportional systematic error. We calculated the amount of analyte added by multiplying the concentration of the analyte added solution by the dilution factor(mL standard)/(mL standard + mL specimen) and took the difference between the sample with addition and the sample with dilution. When making judgments on method performance, the observed that the errors should be compared to the defined allowable error. The average recovery needs to be converted to proportional error(100%/Recovery) and then compared to an analytical quality requirement expressed in percent. The results of recovery experiments were total protein(101.4%), albumin(97.4%), total bilirubin(104%), alkaline phosphatase(89.1%), aspartate aminotransferase(102.8), alanine aminotransferase(103.2), gamma glutamyl transpeptidase(97.6%), creatine kinase(105.4%), lactate dehydrogenase(95.9%), creatinine(103.1%), blood urea nitrogen(102.9%), uric acid(106.4%), total cholesterol(108.5), triglycerides(89.6%), glucose(93%), amylase(109.8), calcium(102.8), inorganic phosphorus(106.3%). We then compared the observed error to the amount of error allowable for the test. There were no items beyond the CLIA criterion for acceptable performance.

• Proceedings of the KSRS Conference
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• v.2
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• pp.525-528
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• 2006

The remote sensing data can be used to calculate the water depth especially in the clear and shallow water area. In this study, the SPOT data was used for bathymetric mapping in Dong-Sha atoll, located in northern South China Sea. The in situ sea depth was collected by echo sounder as well. A global positioning system was employed to locate the accurate sampling points for sea depth. An empirical model between measurement sea depth and band digital count was determined and based on least squares regression analysis. Both non-classification and unsupervised classification were used in this study. The results show that the standard error is less than 0.9m for non-classification. Besides, the 10% error related to the measurement water depth can be satisfied for more than 85% in situ data points. Otherwise, the 10% relative error can reach more than 97%, 69%, and 51% data points at class 4, 5, and 6 respectively if supervised classification is applied. Meanwhile, we also find that the unsupervised classification can get more accuracy to estimate water depth with standard error less than 0.63, 0.93, and 0.68m at class 4, 5, and 6 respectively.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.4
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• pp.374-383
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• 2020

At ship design stage, the maneuverability is generally estimated based on the empirical formula or the computational fluid dynamic (CFD), which is one of the numerical simulation methods. Using the hydrodynamic derivatives derived through these methods can quantitatively estimate the maneuverability of target vessels and evaluate indirect maneuverability. Nevertheless, research on estimating maneuverability is insufficient for ships not subject to IMO maneuverability standard, especially fishing vessels, and even at the design stage, the empirical formula developed for merchant ships is applied without modification. An estimation error may occur due to the empirical formula derived from the regression analysis results of a model test if the empirical formula developed for merchant ships with different hull shapes is applied to fishing vessels without any modification. In this study, the modified empirical formula that can more accurately estimate the fishing vessel's maneuverability was derived by including the hull shape parameter of target fishing trawlers in the regression analysis process that derives Kijima et al. (1990) formula. As a result, the modified empirical formula showed an average estimation error of 6%, and the result improved the average error of 49% of Kijima et al. (1990) formula developed for merchant ships.

• Journal of Korean Society of Forest Science
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• v.80 no.4
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• pp.379-382
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• 1991

Tree height at age 10 was used to estimate the statistical efficiencies of sampling size in the progeny test of Pinus koraiensis S. et Z. Experimental design was RCB design which consists of 25 half-sib families in each of three blocks. The number of families and blocks were fixed, therefore, the number of trees sampled per plot was the only factor that influences the environmental portion of the family mean height. Coefficient of variation, the estimate of the standard error of the family mean height, decreased with increase of sampling plot size, and became stable from 4-tree plot sampling (6.97%). The experimental error was significant from 7-tree sampling plot size. Nonlinear relationship (${\hat{Y}}=10.425e-^{0.073x}$ ; $R^2$=0.840) was found between the sampling plot size and the standard error of family mean height.