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Bootstrap Confidence Intervals for a One Parameter Model using Multinomial Sampling
Hyeong Chul Jeong,Dae Hak Kim 한국데이터정보과학회 1999 한국데이터정보과학회지 Vol.10 No.2
We Considered a bootstrap method for constructing confidence intervals for a one parameter model using multinomial sampling. The convergence rates of the proposed bootstrap method are calculated for model-based maximum likelihood estimators(MLE) using multinomial sampling. Monte Carlo Simulation was used to compare the performance of bootstrap methods with normal approximations in terms of the average coverage probability criterion.
A Study for Obtaining Weights in Pairwise Comparison Matrix in AHP
Hyeong Chul Jeong,Jong Chan Lee,Myoung Shic Jhun 한국통계학회 2012 응용통계연구 Vol.25 No.3
In this study, we consider various methods to estimate the weights of a pairwise comparison matrix in the Analytic Hierarchy Process widely applied in various decision-making elds. This paper uses a data dependent simulation to evaluate the statistical accuracy, minimum violation and minimum norm of the obtaining weight methods from a reciprocal symmetric matrix. No method dominates others in all criteria. Least squares methods perform best in point of mean squared errors; however, the eigenvectors method has an advantage in the minimum norm.