• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.4
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• pp.641-649
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• 2016

Highly precision body measurements represent basic data required by industry and researches who wish to utilize information about the human body. The proficiency and expertise of the measurers have a significant influence on the error and accuracy of data when various parts from multiple subjects' bodies are measured. Therefore, in order to measure accurate body measurements (when measuring bodies directly), it is necessary to conduct objective analyses on errors. This study calculated the Relative Technical Error of Measurement (%TEM) using data that measured each of 24 subjects and discussed errors and methods to reduce errors by conducting comparison analysis based on measured items and objects. The result of analysis indicated that the errors based on age and gender of the objects of measurement were minor; however, there were comparatively distinct differences in measured errors based on measured items. 'Right and left Shoulder Angle' for all measured subjects displayed the greatest errors and standard deviations. 'Height' dimension, Lateral Malleolus Height and Head Height had big errors; in addition, 'Circumference', Neck Base Circumference and Armscye Circumference also had big errors. More careful measurements of such items with big errors require additional educational plan such as a proposal for more objective and detailed measurement methods. Items with small errors but big standard deviations such as Waist Circumference, Calf Circumference, Minimum Leg Circumference, Chest Circumference, Hip Circumference and Waist Circumference confirmed that errors for them greatly decreased with repeated experiments and resultant measurers increased proficiency; consequently, repeated measuring experiments for these items greatly enhance accuracy.

• Journal of the Korea Society for Simulation
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• v.10 no.3
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• pp.31-46
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• 2001

Repeated measurements on units under different conditions are common in biological and biomedical studies. In a number of growth and pharmacokinetic studies, the relationship between the response and the covariates is assumed to be nonlinear in some unknown parameters and the form remains the same for all units. Nonlinear random coefficient models are used to analyze such repeated measurement data. Extended least squares methods are proposed in the literature for estimating the parameters of the model. However, neither objective function has closed form expression in practice. This paper proposes Monte Carlo methods to estimate the objective functions and the corresponding estimators. A simulation study that compare various methods is included.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.945-957
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• 2009

Repeated measurement data among several treatments with a control is often used in the field of medicine study. In this paper, we suggest a method for comparison of the linear trend of responds followed time among several treatments with a control based on repeated measurement data. First, we estimate slope from each subject and generate samples using the slope estimated previous. And then, we test the difference among treatment with a control by ANOVA F test, Jonckheere-Terpstra test, updated control group procedure using generated samples. Monte Carlo Simulation is adapted to compare the power and experimental significance levels in various configuration.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.1
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• pp.109-116
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• 2001

The detection of corporate failures is a subject that has been particularly amenable to cross-sectional financial ratio analysis. In most of firms, however, the financial data are available over past years. Because of this, a model utilizing these longitudinal data could provide useful information on the prediction of bankruptcy. To correctly reflect the longitudinal and firm-specific data, the generalized linear model with assuming the first order AR(autoregressive) process is proposed. The method is motivated by the clinical research that several characteristics are measured repeatedly from individual over the time. The model is compared with several other predictive models to evaluate the performance. By using the financial data from manufacturing corporations in the Korea Stock Exchange (KSE) list, we will discuss some experiences learned from the procedure of sampling scheme, variable transformation, imputation, variable selection, and model evaluation. Finally, implications of the model with repeated measurement and future direction of research will be discussed.

• Journal for History of Mathematics
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• v.12 no.1
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• pp.1-12
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• 1999

The first part of this thesis discusses the types and the properties of errors, one of which makes up systematic errors of measurements, removable by detecting their causes, the other errors of accidental causes which can not be removed. The final part of this thesis deals with the historical background of the Gaussian distribution by Hershel, Hagen, Laplace and Gauss from the late 18th century to the early 19th century. It can be concluded that the accidental idea and the treatment of accidental error distribution by Gauss Is the best one based on the assumption that the most probable value of true value is the arithmetic mean of data, obtained by repeated measurements of a given quantity.

• Journal of Technologic Dentistry
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• v.41 no.3
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• pp.211-217
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• 2019

Purpose: The purpose of this study is to evaluate the repeated measurement stability of scans related to dentition type. Methods: A normal model and the crowding and diastema models are also duplicated using duplicating silicon. After that, a plaster model is made using a plaster-type plaster on the duplicate mold, and each model is scanned 5 times by using an extraoral scanner. The gingival part and molar part were deleted from the 3D STL file data obtained through scanning. Using the 3D stl file obtained in this way, data is nested between model groups. Thereafter, RMS values obtained were compared and evaluated. The normality test of the data was performed for the statistical application of repeated measurements with dentition type, and the normality was satisfied. Therefore, the one-way ANOVA test, which is a parametric statistical method, was applied, and post-tests were processed by the Scheffe method. Results: The average size of each RMS in the Normal, Diastema, and Crowding groups was Normal> Crowding> Diastema. However, the standard deviation was in the order of Crowding> Normal> Diastema. The average value of each data is as follows. Diastema model was the smallest ($5.51{\pm}0.55{\mu}m$), followed by the crowding model ($12.30{\pm}2.50{\mu}m$). The normal model showed the maximum error ($13.23{\pm}1.06{\mu}m$). Conclusion: There was a statistically significant difference in the repeatability of the scanning measurements according to the dentition type. Therefore, you should be more careful when scanning the normal intense or crowded dentition than scanning the interdental lining. However, this error value was within the range of applicable errors for all clinical cases.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.93-107
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• 2002

Random effects generalised linear models are useful for analysing clustered count data in which responses are usually correlated. We propose a Bayesian approach to parameter estimation and variable selection in random effects generalised linear models for count data. A simple Gibbs sampling algorithm for parameter estimation is presented and a simple and efficient variable selection is done by using the Gibbs outputs. An illustrative example is provided.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.291-299
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• 2008

This paper discusses model building for repeated binary response data with different time-dependent covariates each occasion. Since repeated measurements data are having correlated structure, weighed least squares(WLS) methodology is applied. Repeated measures designs are usually having different sizes of experimental units like split-plot designs. However repeated measures designs differ from split-plot designs in that the levels of one or more factors cannot be randomly assigned to one or more of the sizes of experimental units in the experiment. In this case, the levels of time cannot be assigned at random to the time intervals. Because of this nonrandom assignment, the errors corresponding to the respective experimental units may have a covariance matrix. So, the estimates of effects included in a suggested logit model are obtained by using covariance structures.

• The Research Journal of the Costume Culture
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• v.13 no.1
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• pp.60-74
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• 2005

The purpose of this study was to systematize accurate data about the body measurements of early adolescent boys, as there was a broad individual variance between their bodies, and it's additionally attempted to identify the unique physical characteristics of different age groups. The subjects in this study were boys who were at the ages of 10 to 14 in the capital area. Their bodies were measured by anthropometric and photographic method, and data on 549 boys were analyzed. The total of 100 items(47 anthropometric items, 10 indexed items and 43 photographic items) were selected to be measured. For data analysis were performed descriptive statistics, ANOVA and Duncan test using SPSS Ver. 10. The results of the study are as follows. According to the result of comparing the body measurements of early adolescent boys, a period of active growth and a period of slow growth were repeated alternately as they aged and the age of active growth tended to get younger.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.5
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• pp.193-208
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• 2022

In this study, we proposed a mathematical model that can explain the source of the observed variability of repeated measurement data collected in Korean medicine clinical practice, and conducted a pilot analysis to infer the source of these variability based on our model. Mathematical model was constructed by dividing the observed variations into three components: common time-dependent variations, signal shift, and measurement error. To show the applicability of our model in real data, we analyzed 20 repeated measurement data of Korean clinical indicators in graduate students of Pusan National University Graduate School of Korean Medicine. We showed how to infer each source of variations based on our model and also showed the limitation of inference given the acquired the dataset. On the basis of objective recognition of these source of the variability, we hope that quantitative investigations on these sources for each Korean medicine clinical indicator are made in the future, so that they can be used in the clinical and educational areas of Korean medicine.