• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.155-164
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• 2011

This paper suggests two ratio-cum-product estimators of finite population mean using known coefficient of variation and co-efficient of kurtosis of auxiliary characters. The bias and mean squared error of the proposed estimators with large sample approximation are derived. It has been shown that the estimators suggested by Upadhyaya and Singh (1999) are particular case of the suggested estimators. Almost ratio-cum product estimators of suggested estimators have also been obtained using Jackknife technique given by Quenouille (1956). An empirical study is also carried out to demonstrate the performance of the suggested estimators.

• Animal cells and systems
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• v.7 no.3
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• pp.249-253
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• 2003

Abnormalities in fibrinolysis system is associated with risk of hypertension. In this report, the Alu repeat insertion/deletion (I/D) polymorphism of tissue plasminogen activator (t-PA) and the Hind III RFLP of plasminogen activator inhibitor-1 (PAI-1) genes were investigated in 115 normotensives and 83 patients with hypertension, and their association with anthropometrical data and plasma biochemical parameters were analyzed. There were no significant differences in the gene frequencies of the two candidate genes between normotensives and hypertensives, respectively. Our results indicate lack of associations between the two polymorph isms in t-PA and PAI-1 genes and risk of hypertension in the population under study. However, the Hind III RFLP of PAI-1 gene was significantly associated with plasma glucose level, suggesting its role in glucose metabolism. It needs to be tested whether this RFLP of PAI-1 gene is associated with insulin resistance syndrome or non-insulin dependent diabetes mellitus (NIDDM) in the Korean population.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.169-181
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• 2014

Marginalized random effects models (MREM) are commonly used to analyze longitudinal categorical data when the population-averaged effects is of interest. In these models, random effects are used to explain both subject and time variations. The estimation of the random effects covariance matrix is not simple in MREM because of the high dimension and the positive definiteness. A relatively simple structure for the correlation is assumed such as a homogeneous AR(1) structure; however, it is too strong of an assumption. In consequence, the estimates of the fixed effects can be biased. To avoid this problem, we introduce one approach to explain a heterogenous random effects covariance matrix using a modified Cholesky decomposition. The approach results in parameters that can be easily modeled without concern that the resulting estimator will not be positive definite. The interpretation of the parameters is sensible. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using this method.

• Journal of the Ergonomics Society of Korea
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• v.21 no.1
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• pp.15-26
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• 2002

The anthropometric characteristics of the intended user population are most important parameters in the equipment and workplace layout design, particularly in the airplane cockpit design. Because human body is composed of multi-dimensional body segments, single 'average' or 'extreme' manikin is not sufficient in computer-aided design(CAD) environments. To overcome this limitation, we constructed a manikin group representing Korean Male pilot population. First, we identified 16 anthropometric variables which are important parameters in the evaluation of reach, visibility, and clearance. And we found their correlations and conducted a factor analysis. Four common factors were extracted in the factor analysis. The first one was related with length dimensions, the second was with the arm reach, the third was with the sitting height, and the last was with breadth-depth dimensions. Finally, 17 manikins were constructed and presented in the CAD prototype.

• ALGAE
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• v.24 no.1
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• pp.31-38
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• 2009

The brown algae padina concrescens is widely distributed in the northwestern Pacific Mexico. We described the population of P. concrescens based on population parameters such as cover, density and size structure and reproduc-tion at two levels tide at the intertidal area in the southwestern Baja. California Peninsula. Monthly visits from January to December 2003 were done. Both cover and density were measured in situ by quadrants method. Samples were collected to obtain size structure and percentage of reproductive fronds. Our results show there is sparial vari-ation in the population structure more than temporal. Thus, cover and density peak were at different months ineach tide level studied, the lower tide level shows the high values in cover as well as density. The frond develop-ment was observed in height/width ratio this relation was consistent only in the low tidal zone. Size class distribu-tion has consistently small size plants in both tide levels.Reproduction was seasonal in the tide channel but in both tide levels all the reproductive plants were tetrasporophyte. Our results suggest that this population is pseudopere-nial and it strongly as a consequence of the intense competition in the intertidal zone.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.67-75
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• 1998

In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.2
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• pp.183-188
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• 2022

It is a difficult task to estimate parameters in even a simple stock assessment model such as a surplus production model, using only data about temporal catch-per-unit-effort (CPUE) (or survey index) and fishery yields. Such difficulty is exacerbated when time-varying parameters are treated as random effects (aka state variables). To overcome the difficulty, previous studies incorporated somewhat subjective assumptions (e.g., B₁=K) or informative priors of parameters. A key is how to build an objective joint prior of parameters, reducing subjectivity. Given the limited data on temporal CPUEs and fishery yields from 1999-2020 for common squid Todarodes pacificus, we built a joint prior of only two parameters, intrinsic growth rate (r) and carrying capacity (K), based on the resilience level of the population (Froese et al., 2017), and used a Bayesian state-space production assessment model. We used template model builder (TMB), a R package for implementing the assessment model, and estimating all parameters in the model. The predicted annual biomass was in the range of 0.76×10⁶ to 4.06×10⁶ MT, the estimated MSY was 0.13×10⁶ MT, the estimated r was 0.24, and the estimated K was 2.10×10⁶ MT.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.539-547
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• 2005

We propose the Bayesian testing for the equality of two independent Inverse Gaussian population means using the fractional Bayesian factors suggested by O' Hagan(1995). As prior distribution for the parameters, we assumed the noninformative priors. In order to investigate the usefulness of the proposed Bayesian testing procedures, the behaviors of the proposed results are examined via real data analysis.

• Journal of Korean Society for Quality Management
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• v.13 no.2
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• pp.29-36
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• 1985

The problem of selecting the gamma population with the largest mean out of k gamma populations, each of which has the same shape parameter is considered. An elimination type two-stage procedure is proposed which guarantees the same probability requirement using the indifference-zone approach as does the single-stage procedure of Gibbons, Olkin and Sobel (1977). The two-stage procedure has the highly desirable property that the expected total number of observations required by the procedure is always less than that of the corresponding single-stage procedure regardless of the configuration of the population parameters.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.330-335
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• 1995

The rebustness of one sample Chi-square test for multivariate normal mean vector is investigated when the multivariate normal population is mixed with another multivariate normal population with differing in the mean vector. Explicit expressions for the level of significance and power of the test are derived. Some numerical results indicate that the Chi-square test procedure is quite robust against slight mixtures of multivariate normal populations differing in location parameters.