• The Korean Journal of Applied Statistics
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• v.8 no.2
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• pp.99-108
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• 1995

When modeling a complicated system with a computer model, it is of vital importance to choos input values efficiently. The Latin Hypercube sampling (LHS) proposed by MaKay et al.(1979) has been most widely used for choosing input values for a computer model. We propose the two-stage Latin Hypercube sampling(TLHS) which is an improved version of the LHS for procucing input values in estimating the excectation of a function of the output variable. The proposed method is applied to simulation study of the performance of a printer actuator and it is shown to outperform the other sampling methods including the LHS in accuracy.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.65-70
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• 2008

The capacity spectrum method (CSM) is a deterministic seismic analysis approach wherein the expected seismic response of a structure is established as the intersection of the demand and capacity curves. Recently, there are a few studies about a probabilistic CSM where uncertainties in design factors such as material properties, loads, and ground motion are being considered. However, researches show that soil-structure interaction also affects the seismic responses of structures. Thus, their uncertainties should also be taken into account. Therefore, this paper presents a probabilistic approach of using the CSM for seismic analysis considering uncertainties in soil properties. For application, a reinforced concrete bridge column structure is employed as a test model. Considering the randomness of the various design parameters, the structure's probability of failure is obtained. Monte Carlo importance sampling is used as the tool to assess the structure's reliability when subjected to earthquakes. In this study, probabilistic CSM with and without consideration of soil uncertainties are compared and analyzed. Results show that the analysis considering soil structure interaction yields to a greater probability of failure, and thus can lead to a more conservative structural design.

• Journal of Forest and Environmental Science
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• v.33 no.4
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• pp.295-304
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• 2017

This study compared and analyzed the construction of a land use change matrix for the Intergovernmental Panel on Climate Change's (IPCC) land use, land use change, and forestry area (LULUCF). We used National Forest Inventory (NFI) permanent sample plots (with a sample intensity of 4 km) and permanent sample plots with 500 m sampling intensity. The land use change matrix was formed using the point sampling method, Level-2 Land Cover Maps, and forest aerial photographs (3rd and 4th series). The land use change matrix using the land cover map indicated that the annual change in area was the highest for forests and cropland; the cropland area decreased over time. We evaluated the uncertainty of the land use change matrix. Our results indicated that the forest land use, which had the most sampling, had the lowest uncertainty, while the grassland and wetlands had the highest uncertainty and the least sampling. The uncertainty was higher for the 4 km sampling intensity than for the 500 m sampling intensity, which indicates the importance of selecting the appropriate sample size when constructing a national land use change matrix.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.9_10 s.157
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• pp.1366-1377
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• 2006

The purposes of this study was to find out 1) the difference in the importance degree of store attribute according to interaction between fashion product types and task situations, 2) the difference in the importance degree of store attribute according to the patronized store types on the basis of fashion product types. The subjects were female adults who lived in Seoul. The sampling method was quota sampling. The data was obtained from 391 questionnaires. The data were analyzed using frequency, one-way ANOVA, Duncan test, and two-way ANOVA by means of SPSS. The results were as follows; 1. According to fashion product types and task situations, there were significant differences in factors of product features, services, physical environments of the store, and price. 2. According to interaction between fashion product types and task situations, there were significant differences in factors of product features, services, physical environments of the store, and price. 3. There were significant differences in factors of product features, services, physical environments of the store, and location of store according to patronized store types, when a consumer purchased a suit, casual wear and inner wear.

• Journal of Korean Academy of Nursing
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• v.18 no.2
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• pp.118-127
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• 1988

This descriptive-correlational study was undertaken in order to examine if there was relationship between health locus of control and health behavior of 122 residents in Choong Nam Province. The sampling method was non-probability, conventent sampling technique. Questionnaire survey was conducted from March 2 to March 11, 1988. Each participant completed the Multidimensional Health Locus of control(MHLC) scale (Wallsten & Wallston, 1978) and Health Behavior scale (developed by Dr. cho) The collected data were analyzed using Peason Correlation coefficient, t-test and Analusis of Variance. The results were as follows : 1. Hypothesis 1, stating that the higer the score of internal health locus of control, the higher the Score of level of actual implementation of health behavior was supported(r＝.1344, p＜.05). 2. Hypothesis 2, stating that the higher the score of chance health locus of control, the lower the score of level of actual implementation of health behavior was not supported (r＝-.1344, p＞.05). 3. Hypothesis 3, stating that the higher the score of internal health locus of control, the higher the score of the level of perceived importance of health behavior was supported (r＝.3373, p＜.001). 4. Hypothesis 4, stating that the higher the score of chance health locus of control, the lower the score of level of perceived importance of health behavior was not supported (r＝-.0810, p＞.05). 5. The mean score of internal was 23.36, powerful others was 19.04 and chance 15.36 out of maximum range of 6-30 respectively. The mean score of level of actual implementation of health behavior was 112.84 and level of perceived importance of health behavior 143.60 our of maximum range of 32-160 respectively. 6. The variances which were related with the level of actual implementation of health behavior, were education level, occupation, economic status, referred method of primary health, management and resicent's place. And the variance which were related with the level of perceived importance of health behavior were sex, economic status and occupation.

• Korean Journal of Clinical Pharmacy
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• v.23 no.2
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• pp.137-141
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• 2013

Purpose: This study compared the performance of new NONMEM estimation methods using a population analysis dataset collected from a clinical study that consisted of 40 individuals and 567 observations after a single oral dose of glimepiride. Method: The NONMEM 7.2 estimation methods tested were first-order conditional estimation with interaction (FOCEI), importance sampling (IMP), importance sampling assisted by mode a posteriori (IMPMAP), iterative two stage (ITS), stochastic approximation expectation-maximization (SAEM), and Markov chain Monte Carlo Bayesian (BAYES) using a two-compartment open model. Results: The parameters estimated by IMP, IMPMAP, ITS, SAEM, and BAYES were similar to those estimated using FOCEI, and the objective function value (OFV) for diagnosing the model criteria was significantly decreased in FOCEI, IMPMAP, SAEM, and BAYES in comparison with IMP. Parameter precision in terms of the estimated standard error was estimated precisely with FOCEI, IMP, IMPMAP, and BAYES. The run time for the model analysis was shortest with BAYES. Conclusion: In conclusion, the new estimation methods in NONMEM 7.2 performed similarly in terms of parameter estimation, but the results in terms of parameter precision and model run times using BAYES were most suitable for analyzing this dataset.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.479-485
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• 2009

When a part of data is unobserved the marginal likelihood of parameters given the observed data often involves analytically intractable high dimensional integral and hence it is hard to find the maximum likelihood estimate of the parameters. Simulated maximum likelihood(SML) method which estimates the marginal likelihood via Monte Carlo importance sampling and optimize the estimated marginal likelihood has been used in many applications. A key issue in SML is to find a good proposal density from which Monte Carlo samples are generated. The optimal proposal density is the conditional density of the unobserved data given the parameters and the observed data, and attempts have been given to find a good approximation to the optimal proposal density. Algorithms which adaptively improve the proposal density have been widely used due to its simplicity and efficiency. In this paper, we describe a fully adaptive algorithm which has been used by some practitioners but has not been well recognized in statistical literature, and evaluate its estimation performance and robustness via a simulation study. The simulation study shows a great improvement in the order of magnitudes in the mean squared error, compared to non-adaptive or partially adaptive SML methods. Also, it is shown that the fully adaptive SML is robust in a sense that it is insensitive to the starting points in the optimization routine.

• Journal of the Korean Geotechnical Society
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• v.16 no.1
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• pp.145-155
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• 2000

Orientation data of discontinuities are of paramount importance for rock slope stability studies because they control the possibility of unstable conditions or excessive deformation. Most orientation data are collected by using linear sampling techniques, such as borehole fracture mapping and the detailed scanline method (outcrop mapping). However, these data, acquired by the above linear sampling techniques, are subjected to bias, owing to the orientation of the sampling line. Even though a weighting factor is applied to orientation data in order to reduce this bias, the bias will not be significantly reduced when certain sampling orientations are involved. That is, if the linear sampling orientation nearly parallels the discontinuity orientation, most discontinuities orientation data which are parallel to sampling line will be excluded from the survey result. This phenomenon can cause serious misinterpretation of discontinuity orientation data because critical information is omitted. In the case study, orientation data collected by using the borehole fracture mapping method (vertical scanline) were compared to those based on orientation data from the detailed scanline method (horizontal scanline). Differences in results for the two procedures revealed a concern that a representative orientation of discontinuities was not accomplished. Equal-area, polar stereo nets were used to determine the distribution of dip angles and to compare the data distribution fur the borehole method versus those for the scanline method.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1103-1118
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• 2009

When we wish to estimate the mean or total of a finite population, the numbering of the population units is of importance. In this paper, we have proposed two methods for estimating the mean or total of a population having a linear trend, for the case when the reciprocal of the sampling fraction is an even number and the sample size is an odd number. The first method involves drawing a sample by using a method which is a generalization of Singh et al's (1968) modified systematic sampling, and using interpolation in determining the estimator. The second method involves selecting a sample by modified systematic sampling, and estimating the population parameters by the regression estimation method. Under the criterion of the expected mean square error based on Cochran's (1946) infinite superpopulation model, the proposed methods have been compared with existing methods. We have also made a comparison between the two proposed methods.

• Journal of Applied Tourism Food and Beverage Management and Research
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• v.17 no.1
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• pp.85-107
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• 2006

The study reviewed a literature on a role and importance of physical environment of a hotel service and determinant factors including a renovation. Also, the study showed how the service physical environment influenced satisfaction, quality of relationship, and customer loyalty. The Sampling method used a convenience sampling of non-probability sampling methods. samples were from domestic customers experience the hotel services for more than five years of the 1st deluxe hotel in seoul. The collected 200 questionnaires were used to analyze empirically. Regression analysis were used to verify hypotheses. Service physical environment influenced satisfaction, quality of relationship and customer loyalty significantly.