• Journal of Korean Society for Quality Management
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• v.23 no.4
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• pp.42-51
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• 1995

The bootstrap confidence intervals are a computer-based method for assigning measures of accuracy to statistical estimators. In this paper we examine the small sample behavior of the Kaplan-Meier and Nelson-type estimators for the survival function using the bootstrap and asymptotic normal-theory confidence intervals. The Nelson-type estimator is nearly always better than the Kaplan-Meier estimator in the sense of achieved error rates. From the point of confidence length, the reverse is true. Also, we show that the bootstrap confidence intervals are better than the asymptotic normal-theory confidence intervals in terms of achieved error rates and confidence length.

• Bulletin of the Korean Mathematical Society
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• v.45 no.1
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• pp.157-167
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• 2008

A mapping f : $M{\rightarrow}N$ between Hilbert $C^*$-modules approximately preserves the inner product if $$\parallel<f(x),\;f(y)>-<x,y>\parallel\leq\varphi(x,y)$$ for an appropriate control function $\varphi(x,y)$ and all x, y $\in$ M. In this paper, we extend some results concerning the stability of the orthogonality equation to the framework of Hilbert $C^*$-modules on more general restricted domains. In particular, we investigate some asymptotic behavior and the Hyers-Ulam-Rassias stability of the orthogonality equation.

• International Journal of Reliability and Applications
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• v.15 no.2
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• pp.99-110
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• 2014

Based on the kernel function, a new test is presented, testing $H_0:\bar{F}$ is exponential against $H_1:\bar{F}$ is UBACT and not exponential is given in section 2. Monte Carlos null distribution critical points for sample sizes n = 5(5)100 is investigated in section 3. The Pitman asymptotic efficiency for common alternatives is obtained in section 4. In section 5 we propose a test statistic for censored data. Finally, a numerical examples in medical science for complete and censored data using real data is presented in section 6.

• Korean Journal of Computational Design and Engineering
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• v.8 no.4
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• pp.262-269
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• 2003

An approach to compute characteristic curves such as section curves, reflection characteristic lines, and asymptotic curves on a surface is introduced. Each problem is formulated as a surface-plane inter-section problem. A single-valued function that represents the characteristics of a problem constructs a property surface on parametric space. Using a contouring algorithm, the property surface is intersected with a horizontal plane. The solution of the intersection yields a series of points which are mapped into object space to become characteristic curves. The approach proposed in this paper eliminates the use of traditional searching methods or non-linear differential equation solvers. Since the contouring algorithm has been known to be very robust and rapid, most of the problems are solved efficiently in realtime for the purpose of visualization. This approach can be extended to any geometric problem, if used with an appropriate formulation.

• Journal of Korean Society for Quality Management
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• v.22 no.4
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• pp.59-78
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• 1994

In some observational studies, we have often random censoring model. However, the data available may be partially observable censored data consisting of the observed failure times and only those nonfailure times which are subject to follow up. In this paper, we present an extension of the problem of partially parametric estimation of the survival function to such partially observable censored data. The proposed estimator treats the observed failure times nonparametrically and uses a parametric model only for those nonfailure times which are subject to follow-up. We discuss the motivation and construction of the proposed estimator and investigate the limiting properties of the proposed estimator such as asymptotic normality. Also, when the assumed parametric model is exponential, the asymptotic variance of the estimator is obtained. Furthermore, an example is given to compare the proposed estimator with the modified Kaplan Meier(MKM) estimator. From the results, it is shown that the relative efficiency of the proposed estimator is higher than that of the MKM estimator in the follow-up study with increasing time.

• Bulletin of the Korean Mathematical Society
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• v.53 no.5
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• pp.1327-1339
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• 2016

Self-organizing systems arise very naturally in artificial intelligence, and in physical, biological and social sciences. In this paper, we modify the classic Cucker-Smale model at both microscopic and macroscopic levels by taking the target motion pattern driving forces into consideration. Such target motion pattern driving force functions are properly defined for the line-shaped motion pattern and the ball-shaped motion pattern. For the modified Cucker-Smale model with the prescribed line-shaped motion pattern, we have analytically shown that there is a flocking pattern with an asymptotic flocking velocity. This is illustrated by numerical simulations using both symmetric and non-symmetric pairwise influence functions. For the modified Cucker-Smale model with the prescribed ball-shaped motion pattern, our simulations suggest that the solution also converges to the prescribed motion pattern.

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.289-301
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• 1996

This paper suggests a new diagnostic measure for detecting influential observations in two group linear discriminant analysis(LDA). It is developed from an information theoretic point of view using the minimum discrimination information(MDI) methodology. MDI estimator of symmetric divergence by Kullback(l967) is taken as a measure of the power of discrimination in LDA. It is shown that the effect of an observation over the power of discrimination is fully explained by the diagnostic measure. Asymptotic distribution of the proposed measure is derived as a function of independent chi-squared and standard normal variables. By means of the distributions, a couple of methods are suggested for detecting the influential observations in LDA. Performance of the suggested methods are examined through a simulation study.

• Journal of the Korean Statistical Society
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• v.34 no.1
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• pp.49-60
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• 2005

In this paper we introduce a nonparametric method for estimating the probability density function of detection distances in line transect sampling. The estimator is obtained using a frequency histogram density estimation method. The asymptotic properties of the proposed estimator are derived and compared with those of the kernel estimator under the assumption that the data collected satisfy the shoulder condition. We found that the asymptotic mean square error (AMSE) of the two estimators have about the same convergence rate. The formula for the optimal histogram bin width is derived which minimizes AMSE. Moreover, the performances of the corresponding k-nearest-neighbor estimators are studied through simulation techniques. In the absence of our knowledge whether the shoulder condition is valid or not a new semi-parametric model is suggested to fit the line transect data. The performances of the proposed two estimators are studied and compared with some existing nonparametric and semiparametric estimators using simulation techniques. The results demonstrate the superiority of the new estimators in most cases considered.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.1 no.1
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• pp.81-86
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• 1989

Herein the surge-heave-pitch motion of a ship has been analyzed within the framework of linear potential theory. The ship is assumed slender weakly moored along the centerline of a rectangular harbor with constant depth and straight coastline. The method of matched asymptotic expansion is us-ed to obtain the leading-order solution. The ship and harbor responses to incident long waves can be re-presented in terms of Green's function, which is the solution of the Helmholtz equation satisfying necessary boundary conditions. Numerical results clearly indicate the importance of the surge motion.

• Journal of the Society of Naval Architects of Korea
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• v.29 no.2
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• pp.1-7
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• 1992

The computational methods of extreme sea level are developed in this study. Based on type III asymptotic distribution, non-linear multiple regression method, skewness method and maximum likelihood method are used to evaluate the parameters of the distribution. The difference between real data and evaluated distribution function is fitted to get more desirable accuracy by employing polynominals. The numerical examples are given in the last section in order to illustrate the application of the present scheme.