• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.95-104
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• 2017

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.887-894
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• 2009

The two-stage minimum risk point estimation of mean, the probability of success in a sequence of Bernoulli trials, is considered for the case where loss is taken to be symmetrized relative squared error of estimation, plus a fixed cost per observation. First order asymptotic expansions are obtained for large sample properties of two-stage procedure. Monte Carlo simulation is carried out to obtain the expected sample size that minimizes the risk and to examine its finite sample behavior.

• Computers and Concrete
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• v.7 no.5
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• pp.469-482
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• 2010

This paper investigates the concrete permeability through a numerical and statistical approach. Concrete is considered as a random heterogeneous composite of three phases: aggregates, interfacial transition zones (ITZ) and matrix. The paper begins with some classical bound and estimate theories applied to concrete permeability and the influence of ITZ on these bound and estimate values is discussed. Numerical samples for permeability analysis are established through random aggregate structure (RAS) scheme, each numerical sample containing randomly distributed aggregates coated with ITZ and dispersed in a homogeneous matrix. The volumetric fraction of aggregates is fixed and the size distribution of aggregates observes Fuller's curve. Then finite element method is used to solve the steady permeation problem on 2D numerical samples and the overall permeability is deduced from flux-pressure relation. The impact of ITZ on overall permeability is analyzed in terms of ITZ width and contrast ratio between ITZ and matrix permeabilities. Hereafter, 3680 samples are generated for 23 sample sizes and 4 contrast ratios, and statistical analysis is performed on the permeability dispersion in terms of sample size and ITZ characteristics. By sample theory, the size of representative volume element (RVE) for permeability is then quantified considering sample realization number and expected error. Concluding remarks are provided for the impact of ITZ on concrete permeability and its statistical characteristics.

• Journal of Integrative Natural Science
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• v.14 no.3
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• pp.123-129
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• 2021

This research examines the effect of positively skewed population distribution on the two sample t-test through simulation. For simulation work, two independent samples were selected from the same chi-square distributions with 3, 5, 10, 15, 20, 30 degrees of freedom and sample sizes 3, 5, 10, 15, 20, 30, respectively. Chi-square distribution is largely skewed to the right at small degrees of freedom and getting symmetric as the degrees of freedom increase. Simulation results show that the sampled populations are distributed positively skewed like chi-square distribution with small degrees of freedom, the F-test for the equality of variances shows poor performances even at the relatively large degrees of freedom and sample sizes like 30 for both, and so it is recommended to avoid using F-test. When two population variances are equal, the skewness of population distribution does not affect on the t-test in terms of the confidence level. However even though for the highly positively skewed distribution and small sample sizes like three or five the t-test achieved the nominal confidence level, the error limits are very large at small sample size. Therefore, if the sampled population is expected to be highly skewed to the right, it will be recommended to use relatively large sample size, at least 20.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.667-677
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• 2007

In this paper, we propose a method for estimating the mean of a population which has a linear trend, when both n, the sample size, and k, the reciprocal of the sampling fraction, are odd numbers. The proposed method, not having the drawbacks of centered systematic sampling, centered modified sampling and centered balanced sampling, consists of selecting a sample by balanced systematic sampling and estimating the population mean by using interpolation. We compare the efficiency of the proposed method and existing methods under the criterion of the expected mean square error based on the infinite superpopulation model.

• Genomics & Informatics
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• v.7 no.3
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• pp.148-151
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• 2009

Generally, larger sample size leads to a greater statistical power to detect a significant difference. We may increase the sample size for both case and control in order to obtain greater power. However, it is often the case that increasing sample size for case is not feasible for a variety of reasons. In order to look at change in power as the ratio of control to case varies (1:1 to 4:1), we conduct association tests with simulated data generated by PLINK. The simulated data consist of 50 disease SNPs and 300 non-disease SNPs and we compute powers for disease SNPs. Genetic Power Calculator was used for computing powers with varying the ratio of control to case (1:1, 2:1, 3:1, 4:1). In this study, we show that gains in statistical power resulting from increasing the ratio of control to case are substantial for the simulated data. Similar results might be expected for real data.

• Journal of Korean Society for Quality Management
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• v.18 no.1
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• pp.77-83
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• 1990

A model for the economic design of an pn control charts with an assignale cause is presented and the loss-cost function for control schemes using these charts is derived. By minimizing this function with respect to the three control variables, namely, the sample size, the sampling interval and acceptance number, the economically optimal control plan can be optained. The article shows what influence increasing or decreasing condition, according to changeability of the size of these factors, of expected cost can have on the economy when an attribute control chart is used.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.539-557
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• 1998

This article is concerned with the problem of estimating the mean of a one-parameter exponential family through sequential sampling in three stages under quadratic error loss. This more general framework differs from those considered by Hall (1981) and others. The differences are : (i) the estimator and the final stage sample size are dependent; and (ii) second order approximation of a continuously differentiable function of the final stage sample size permits evaluation of the asymptotic regret through higher order moments. In particular, the asymptotic regret can be expressed as a function of both the skewness $\rho$ and the kurtosis $\beta$ of the underlying distribution. The conditions on $\rho$ and $\beta$ for which negative regret is expected are discussed. Further results concerning the stopping variable N are also presented. We also supplement our theoretical findings wish simulation results to provide a feel for the triple sampling procedure presented in this study.

• Journal of Korean Society for Quality Management
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• v.16 no.2
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• pp.82-91
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• 1988

This study is concerned with the development of the economic sampling inspection plan when it is possible to carry out a nondestructive screening inspection for the rejected lots by substitutive characteristics closely related to the original quality characteristic. It is assumed that the probabilities of those errors depend linearly on the fraction defective of the process. The inspection policies considered are as follows: take a random sample of size n from the lot of size N and perform a destructive test on the sample. If the number of the defective items is not greater than a critical number C, accept the lot. Expected cost functions are obtained when the process average can be considered to follow a beta distribution and the way of finding the optimal values of (n, c) is to be explored.

• International Journal of Reliability and Applications
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• v.12 no.1
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• pp.61-77
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• 2011

One of the most important problems in the estimation of the parameter of the failure model, is the cost of experimental sampling units, which can be reduced by using any prior information available about ${\theta}$, and devising a two-stage pooling shrunken estimation procedure. We have proposed an estimator of the reliability function (R(t)) of the exponential model using two-stage time censored data when a prior value about the unknown parameter (${\theta}$) is available from the past. To compare the performance of the proposed estimator with the classical estimator, computer intensive calculations for bias, mean squared error, relative efficiency, expected sample size and percentage of the overall sample size saved expressions, were done for varying the constants involved in the proposed estimator (${\tilde{R}}$(t)).