• Asian-Australasian Journal of Animal Sciences
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• v.25 no.11
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• pp.1511-1514
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• 2012

Epistasis that may explain a large portion of the phenotypic variation for complex economic traits of animals has been ignored in many genetic association studies. A Baysian method was introduced to draw inferences about multilocus genotypic effects based on their marginal posterior distributions by a Gibbs sampler. A simulation study was conducted to provide statistical powers under various unbalanced designs by using this method. Data were simulated by combined designs of number of loci, within genotype variance, and sample size in unbalanced designs with or without null combined genotype cells. Mean empirical statistical power was estimated for testing posterior mean estimate of combined genotype effect. A practical example for obtaining empirical statistical power estimates with a given sample size was provided under unbalanced designs. The empirical statistical powers would be useful for determining an optimal design when interactive associations of multiple loci with complex phenotypes were examined.

• Survey Research
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• v.1 no.1
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• pp.109-123
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• 2000

In order to make statistically valid inferences for survey data based on complex probability sample designs, survey researchers must incorporate the sample design in the data analysis If this in not the case the variance estimates of survey statistics derived under the usual simple random sampling assumptions from an infinite population generally underestimate the true variance, which results in high Type l error level. In this article we introduce new software packages dedicated to analyze complex survey data In particular, we summarize analysis capabilities on SUDAAN Version 7.5 and SAS Version 8.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.291-301
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• 2020

Purpose - The purpose of this study was to investigate several household characteristics related to panel attrition, examining how they may have conditioned the panel data in the Korea Welfare Panel Study (KOWEPS). Design/methodology/approach - We studied the cause of the differences in household income between the original panel and the new panel in KOWEPS. Findings - To summarize our findings, whereas it is highly likely that a low-income household or a household without health insurance will remain in the panel, it is highly likely that a high-income household or a household of more than three members will be taken off the panel. Research implications or Originality - The proportion of low-income household tends to decrease over the years, which appears to result from an overall increase in household income. Such changes are reflected in the pattern in which older panels have higher estimates of household income than newer panels.

• Journal of Preventive Medicine and Public Health
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• v.39 no.4
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• pp.317-324
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• 2006

Objectives: This study was conducted to estimate the unbiased smoking prevalence and its standard errors among adolescents in a large city in Korea, by design-based analysis. Methods: All the students in Daegu city were stratified by grade, gender and region, and then schools as primary sampling units (PSU) were selected by probability proportional to size (PPS) sampling. One or two classes were sampled randomly from each grade, from 5th grade in elementary schools to the 3rd grade in high schools. The students anonymously completed a standardized self-administered questionnaire from October to December 2004. The total number of respondents was 8,480 in the final analysis, excluding the third graders in the general high schools because of incomplete sampling. The sampling weight was calculated for each student after post-stratification adjustment, with adjustment being made for the missing cases. The data were analyzed with Stata 8.0 with consideration of PSU, weighting and the strata variables. Results: The smoking prevalence (%) and standard errors for male students from the fifth grade in elementary schools to the second grade in high schools were $0.93{\pm}0.47,\;1.83{\pm}0.74,\;3.16{\pm}1.00,\;5.12{\pm}1.02,\;10.86{\pm}1.13,\;15.63{\pm}2.44\;and\;17.96{\pm}2.67$, and those for the female students were $0.28{\pm}0.28,\;1.17{\pm}0.73,\;3.13{\pm}0.60,\;1.45{\pm}0.58,\;3.94{\pm}0.92,\;8.75{\pm}1.86\;and\;10.04{\pm}1.70$, sequentially. Conclusions: The smoking prevalence from this study was much higher than those from the other conventional studies conducted in Korea. The point estimates and standard errors from the design-based analysis were different from those of the model-based analysis. These findings suggest the importance of design-based analysis to estimate unbiased prevalence and standard errors in complex survey data and this method is recommended to apply to future surveys for determining the smoking prevalence for specific population.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.501-506
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• 2001

Computer modeling is essential to evaluate possible design of suspension for a railway vehicles. By creating a simulation, the engineers are able to assess the feasibility of a given design and change the design factors to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have turned to surrogate modeling. A surrogate model is essentially a regression performed on a data sampling of the simulation. In the most general sense, metamodels(surrogate model) take the form $y(x)=f(x)+{\varepsilon}$, where y(x) is the true simulation output, f(x) is the metamodel output, and $\varepsilon$ is the error between the two. In this paper, a second order polynomial equation is partially used as a metamodel to represent the forty-six dynamic performances for high speed train. The number of factors as design variables of the metamodel is twenty-nine, which are composed the dynamic characteristics of suspension. This metamodel is used to search the optimum values of suspension characteristics which minimize the dynamic responses for high speed train. This optimization is a multi-objective problem which have many design variables. This paper shows that the response surface model which is made through the design of analysis of computer experiments method is very efficient to solve this complex optimization problem.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.587-597
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• 2018

Procedures, such as sampling technique, survey method, and questionnaire preparation, are required in order to obtain sample data in accordance with the purpose of a survey. An important procedure is the decision of the sample size formula. The sample size formula is determined by setting the target error and total cost according to the sampling method. In this paper, we propose a sample size formula using population changes over time, estimation error of the previous time and response rate of past data when the target error and the expected response rate are given in the simple random sampling. In actual research, we use estimators that apply complex weights in addition to design-based weights. Therefore, we induce a sample size formula for estimators using design-based weights and nonresponse adjustment coefficients, that can be a formula that reflects differences in response rates when survey methods are changed over time. In addition, we use simulations to compare the proposed formula with the existing sample size formula.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.7
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• pp.542-549
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of given design variables and chance them to get a bettor design. Even though commercial simulation codes are used, the computational time and cost remains non-trivial. Therefore, malty researchers have used a mesa model made by sampling data through simulation. In this paper, four mesa-models for each index group such as ride comfort, derailment Quotient, unloading radio and stability index, are constructed by use of neural network. After these meta models are constructed, multi-objective optimization are achieved by using the differential evolution. This paper shows that the optimization of design variables using the neural network model is very efficient to solve the complex optimization Problem.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.553-566
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• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper considers design-based estimators of the parameters of these variance functions based on the within-unit sample variances. This paper devotes to: (1) define an error scale factor $\delta$; (2) develop estimators of the parameters of the linear measurement error variance function of the true values under large-sample and small-error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U.S. Third National Health and Nutrition Examination Survey (NHANES III).

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2231-2233
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• 2002

Recently NCS(Networked Control System) is widely used in distributed control system design. The insertion of the network in the feedback control loop makes the analysis and design of NCS complex. Especially, the network-induced delay can vary the system stability and even destabilizes the entire control system. This paper deals with the stability analysis method of NCS. Also, we analyze the influence of sampling period and network-induced delay on power plant stability.

• Journal of Mechanical Science and Technology
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• v.15 no.1
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• pp.1-9
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• 2001

The recent spread of scaled telemanipulation into microsurgery and the nano-world increasingly requires the identification of the possible operation region as a main system specification. A teleoperation system is a complex cascaded system since the human operator, master, slave, and communication are involved bilaterally. Hence, a small time delay inside a master and slave system can be critical to the overall system stability even without communication time delay. In this paper we derive an upper bound of the scaling product of position and force by using Llewellyns unconditional stability. This bound can be used for checking the validity of the designed bilateral controller. Time delay from the sample and hold of computer control and its effects on stability of scaled teleoperation are modeled and simulated based on the transfer function of the teleoperation system. The feasible operation region in terms of position and force scaling decreases sharply as the sampling rate decreases and time delays inside the master and slave increase.