• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.435-442
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• 2005

The mathematical properties of the sequentially operated systems are often described by integral equations. Reservoir system of a product and sequential probability ratio test (SPRT) are typical examples of sequentially operated systems. When the underlying random quantities follow Erlang distribution, a systematic method was developed to solve the integral equations. We extend the method to the cases having accrual functions of more general types. The solutions of the integral equations are represented as a linear combination of distribution functions, and the coefficients of the linear combination are obtained by solving linear system derived from the continuity condition of the solutions.

• 한국정밀공학회지
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• 제15권9호
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• pp.15-24
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• 1998

In this research. we have proposed a new technique to determine .the combination of design parameters for the process design of a hot forged product using artificial neural network(ANN) and statistical design of experiments(DOE). The investigated problem involves the adequate selection of the aspect ratio of billet, the ram velocity and the friction factor as design parameters. An optimal billet satisfying the forming limitation, die filling, load and energy as well as more uniform distribution of effective strain, is determined by applying the ability of artificial neural network and considering the analysis of mean and variation on the functional requirement. This methodology will be helpful in designing and controlling parameters on the shop floor which would yield the best design solution.

• Journal of Information Processing Systems
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• 제4권1호
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• pp.27-32
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• 2008

Minimum support and confidence have been used as criteria for generating association rules in all association rule mining algorithms. These criteria have their natural appeals, such as simplicity; few researchers have suspected the quality of generated rules. In this paper, we examine the rules from a more rigorous point of view by conducting statistical tests. Specifically, we use contingency tables and chi-square test to analyze the data. Experimental results show that one third of the association rules derived based on the support and confidence criteria are not significant, that is, the antecedent and consequent of the rules are not correlated. It indicates that minimum support and minimum confidence do not provide adequate discovery of meaningful associations. The chi-square test can be considered as an enhancement or an alternative solution.

• Communications for Statistical Applications and Methods
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• 제28권3호
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• pp.307-314
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• 2021

We showed that any missing mechanism is reproduced by EMAR or MNAR with equal fit for observed likelihood if there are non-negative solutions of maximum likelihood equations. This is a generalization of Molenberghs et al. (2008) and Jeon et al. (2019). Nonetheless, as MCAR becomes a nested model of MNAR, a natural question is whether or not MNAR and MCAR are testable by using the well-known three statistics, LR (Likelihood ratio), Wald, and Score test statistics. Through simulation studies, we compared these three statistics. We investigated to what extent the boundary solution affect tesing MCAR against MNAR, which is the only testable pair of missing mechanisms based on observed likelihood. We showed that all three statistics are useful as long as the boundary proximity is far from 1.

• Communications for Statistical Applications and Methods
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• 제31권5호
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• pp.487-496
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• 2024

In this paper, we propose a data-driven procedure to segment a binary sequence as an alternative to the popular hidden Markov model (HMM) based procedure. Unlike the HMM, our procedure does not make any distributional or model assumption to the data. To segment the sequence, we suggest to minimize the least square distance from the observations under total variation regularization to the solution, and develop a polynomial time algorithm for it. Finally, we illustrate the algorithm using a toy example and apply it to the Gemini boat race data between Oxford and Cambridge University. Further, we numerically compare the performance of our procedure to the HMM based segmentation through these examples.

• 산업공학
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• 제11권3호
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• pp.89-101
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• 1998

The randomness of the input variables in simulation experiments produce output responses which are also realizations of random variables. The random responses make necessary the use of statistical inferences to adequately describe the stochastic nature of the output. The analysis of the simulation output of non-terminating simulations is frequently complicated by the autocorrelation of the output data and the effect of the initial conditions that produces biased estimates. The regenerative method has been developed to deal with some of the problems created by the random nature of the simulation experiments. It provides a simple solution to some tactical problems and can produce valid statistical results. However, not all processes can he modeled using the regenerative method. Other processes modeled as regenerative may not return to a given demarcating state frequently enough to allow for adequate statistical analysis. This paper shows how the state transformation concept was successfully used in a queueing model and a job shop model. Although the first example can be analyzed using the regenerative method. it has the problem of too few recurrences under certain conditions. The second model has the problem of no recurrences. In both cases, the state transformation increase the frequency of the demarcating state. It was shown that time state transformations are regenerative and produce more cycles than the best typical discrete demarcating state in a given run length.

• ETRI Journal
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• 제25권5호
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• pp.401-406
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• 2003

A biometric system determines the identity of a person by measuring physical features that can distinguish that person from others. Since biometric features have many variations and can be easily corrupted by noises and deformations, it is necessary to apply machine learning techniques to treat the data. When applying the conventional machine learning methods in designing a specific biometric system, however, one first runs into the difficulty of collecting sufficient data for each person to be registered to the system. In addition, there can be an almost infinite number of variations of non-registered data. Therefore, it is difficult to analyze and predict the distributional properties of real data that are essential for the system to deal with in practical applications. These difficulties require a new framework of identification and verification that is appropriate and efficient for the specific situations of biometric systems. As a preliminary solution, this paper proposes a simple but theoretically well-defined method based on a statistical test theory. Our computational experiments on real-world data show that the proposed method has potential for coping with the actual difficulties in biometrics.

• 한국대기환경학회지
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• 제22권4호
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• pp.421-430
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• 2006

This paper explores a new methodology of socio-statistical survey to classify environmental perception characteristics and to quantify atmospheric environmental sensitivity of neighboring people around a large industrial complex. In order to compensate intrinsic inclination against environmental problems, Atmospheric Environmental Sensitivity Index (AESI) is proposed as the weighted-summation of four representative questions asking the current status of the local air quality, which are chosen by the factor analysis of questionnaire. Atmospheric environmental perception is tried to be classified into interest/indifference characteristics and rational/emotional perception on environmental issues, positive/negative opinion on the solution of environmental problems. According to the chi-square cross-correlation and two-way layout analyses, it was clearly shown that environmental perception is categorized into two major groups, i.e., the positive-rational group having lower AESI and the negative-emotional group having higher AESI which means more seriously senses the status of local air quality.

• 한국국방경영분석학회지
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• 제28권1호
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• pp.17-28
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• 2002

Recently, To define an enterprise's survival strategy is a difficult task, because the competition of a business is a complexity. The reason is the original research uses a statistical survey method. On a conceptual point of SCM, - as an order of development, physical distribution, logistics management, basic supply chain management and advanced supply chain management etc - a supervisor is magnifying gradually as well as confusing with information and analysis techniques which seems possible. On executing this, however, it has many problems since it is hard and wide; therefore, The Manager don't aware a total executable solution even though the enterprise knows necessity of SCM. On this paper focus on a proposal of alternatives with reasonableness of manufacturing and making a profit of sales department like most of enterprises are willing to overcome such as carelessness and unready strategy of investment additionally, the thesis should effort to find an element through an analysis of cases, a statistical method of effective SCM, and actual survey to propose an alternative; moreover, this paper proved the facts that it could be a guiding company, which has an ability of cooperation with entities through the founding of supply chain. As a conclusion, this essay showed the variation that influences the capacity entities and alternative to define an element which basically influence for a cause and effect.

• Communications for Statistical Applications and Methods
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• 제17권2호
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• pp.141-151
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• 2010

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.