• 품질경영학회지
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• 제30권4호
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• pp.106-119
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• 2002

The fatigue curve with estimated parameters represents the estimate of the median or mean life at a given applied stress But, in order to assist a designer in making decisions regarding the fatigue failure mode, it is common practice to construct a design curve on the lower or safe side of data. In this study, to overcome the limitations(i.e., no runout, equal variance, and quality of the approximation, etc) of Shen, Wirsching, and Cashman's method which suggested the approximate design curve for nonlinear models using tolerance interval constructed by Owen's method, an algorithm to find design curves under the fatigue model using a parametric bootstrap method, is proposed and illustrated with multiple fatigue data sets.

• Journal of applied mathematics & informatics
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• 제8권2호
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• pp.361-370
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• 2001

Testing equality of covariance matrix of k populations has long been an interesting issue in statistical inference. To overcome the sparseness of data points in a high-dimensional space and deal with the general cases, we suggest several projection pursuit type statistics. Some results on the limiting distributions of the statistics are obtained. some properties of Bootstrap approximation are investigated. Furthermore, for computational reasons an approximation which is based on Number theoretic method for the statistics is adopted. Several simulation experiments are performed.

• Communications for Statistical Applications and Methods
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• 제15권2호
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• pp.147-159
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• 2008

We consider using ranked set sampling methods to draw inference about the three well-known measures of overlap, namely Matusita's measure $\rho$, Morisita's measure $\lambda$ and Weitzman's measure $\Delta$. Two exponential populations with different means are considered. Due to the difficulties of calculating the precision or the bias of the resulting estimators of overlap measures, because there are no closed-form exact formulas for their variances and their exact sampling distributions, Monte Carlo evaluations are used. Confidence intervals for those measures are also constructed via the bootstrap method and Taylor series approximation.

• Journal of the Korean Statistical Society
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• 제21권1호
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• pp.70-79
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• 1992

The generalized linear models with random regrssors case are studied for bootstrapping. Only the natural link functions are considered. It is shown that the bootstrap approximation to the distribution of the maximum likelihood estimators is valid for almost all sample sequences. A slight extension of this model is also considered.

• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• 응용통계연구
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• 제5권2호
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• pp.271-282
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• 1992

자궁경부암을 대상으로 란게르한스 세포와 악성변화의 연관성을 연구할 때 사용할 수 있는 통계적 방법을 제시하였다. 포아슨 확률모형에 바탕을 두어 설정된 여러 가능한 부모형 가운데 관찰치에 가장 적합한 모형을 AIC유형의 모형선택 기준에 의하여 선택하였다. 모형선택 기준의 표본분포는 불스트？을 이용하여 근사시키고 추정량의 표본분포는 정규근사를 이용하여 구하였다.

• JSTS:Journal of Semiconductor Technology and Science
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• 제16권6호
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• pp.760-770
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• 2016

A 12-bit 750 kS/s Dual-Sampling Successive Approximation Register Analog-to-Digital Converter (SAR ADC) technique with reduced Capacitive DAC (CDAC) is presented in this paper. By adopting the Adaptive Power Control (APC) technique for the two-stage latched type comparator and using bootstrap switch, power consumption can be reduced and overall system efficiency can be optimized. Bootstrapped switches also are used to enhance the sampling linearity at a high input frequency. The proposed SAR ADC reduces the average switching energy compared with conventional SAR ADC by adopting reduced the Most Significant Bit (MSB) cycling step with Dual-Sampling of the analog signal. This technique holds the signal at both comparator input asymmetrically in sample mode. Therefore, the MSB can be calculated without consuming any switching energy. The prototype SAR ADC was implemented in $0.18-{\mu}m$ CMOS technology and occupies $0.728mm^2$. The measurement results show the proposed ADC achieves an Effective Number-of-Bits (ENOB) of 10.73 at a sampling frequency of 750 kS/s and clock frequency of 25 MHz. It consumes only 0.13 mW from a 5.0-V supply and achieves the INL and DNL of +2.78/-2.45 LSB and +0.36/-0.73 LSB respectively, SINAD of 66.35 dB, and a Figures-of-Merit (FoM) of a 102 fJ/conversion-step.