• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10b
• /
• pp.18-23
• /
• 1993

In this paper, we attempt to estimate the state of a finite state system. In such system, we can observe time series data which has some significant behaviors corresponding to its system states. The behavior is characterized by feature parameters extracted from time series. Our thought is that the system output time series data is expressed as a sequence of behavior patterns which are represented by clusters in feature parameters space. An algorithm jointing fuzzy clustering to fuzzy finite state transition model is suggested.

• The Korean Journal of Applied Statistics
• /
• v.4 no.2
• /
• pp.129-135
• /
• 1991

A few simple methods to find $L_1$-unbiased estimators for location and scale parameters are summarized and examples utilizing diffusivity, a natural measure of dispersion for $L_1$-unbiased estimators, are given.

• Journal of the Korean Statistical Society
• /
• v.28 no.3
• /
• pp.325-336
• /
• 1999

Collinearity among independent variables can have severe effects on the precision of response estimation for some region of interest in the experiments with mixture. A method of finding optimal linear restriction on regression parameter in linear model for mixture experiments in the sense of minimizing integrated mean squared error is studied. We use the formulation of optimal restrictions on regression parameters for estimating responses proposed by Park(1981) by transforming mixture components to mathematically independent variables.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.6 no.2
• /
• pp.57-64
• /
• 1981

This paper develops a method of modifying the usual maximum likelihood estimators of the parameters of a two state Markov process when the trajectory of the process can only he observed at regular epochs. The method utilizes the limiting behaviors of the process and the properties of state transition counts. An efficient adaptive strategy to be used together with the modified estimator is also proposed. The properties of the new estimators and the adaptive strategy are investigated using Monte Carlo simulation.

• Communications for Statistical Applications and Methods
• /
• v.8 no.2
• /
• pp.483-497
• /
• 2001

When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

• Communications for Statistical Applications and Methods
• /
• v.4 no.1
• /
• pp.193-205
• /
• 1997

The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers and is subject to abrupt changes in pattern. In this paper we consider the multiprocess discount generalized model with parameters having a dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt change of pattern in parameters.

• Transactions of Materials Processing
• /
• v.6 no.5
• /
• pp.386-394
• /
• 1997

The analysis of the springback is done based on the stress of sheet after forming. Therfore, it is important to get the accurate stress from forming analysis. In this study, some parameters that influence on the accuracy of the springback estimation are investigated. Discretization of sheet and tools, choice of penalty constant and damping in contact treatment, and tool speed scaling are chosen as parameters. As a numerical example, the 2D draw bending benchmark problem of the NUMISHEET'93 is used. Also, the springback results of the s-rail benchmark problem of the NUMISHEET'96 are presented.

• International Journal of Reliability and Applications
• /
• v.6 no.1
• /
• pp.13-29
• /
• 2005

This paper deals with the stochastic analysis of a three-states semi-Markov reliability model. Using both the maximum likelihood and Bayes procedures, the parameters included in this model are estimated. Next, assuming that the lifetime and repair time are generalized exponential random variables, the reliability function of this system is obtained. Then, the distribution of the first passage time of this system is discussed. Finally, some of the obtained results are compared with those available in the literature.

• Journal of the Korean Data and Information Science Society
• /
• v.6 no.2
• /
• pp.41-50
• /
• 1995

In this paper, an iterative procedure, which detects the location of the outliers and the joint estimates of the outliers effects and the model parameters in the autoregressive moving average model with two types of outliers, is proposed. The performance of the procedure is compared with the one in Chen and Liu(1993) through the Monte Carlo simulation. The proposed procedure is very robust in the sense that applies the procedures to the stationary time series model with any types of outliers.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.4
• /
• pp.1091-1102
• /
• 2003

The Weibull variate is commonly used as a lifetime distribution in reliability applications. Estimation of parameters is revisited in the two-parameter Weibull distribution. The method of product spacings, the method of quantile estimates and the method of least squares are applied to this distribution. A comparative study between a simple minded estimate, the maximum likelihood estimate, the product spacings estimate, the quantile estimate, the least squares estimate, and the adjusted least squares estimate is presented.