Few studies have investigated the quantitative relationship between port ownership structure and port efficiency with mixed results. This paper therefore contributes to the empirical literature by investigating the impact of port privatization on port efficiency using sample data drawn from the world's major ports. Moreover, this study applies the Bayesian approach to estimate the impact of port ownership on port efficiency. We fit Bayesian stochastic frontier model which is introduced by Griffin and Steel (2007) by WinBUGS. World's 25 main ports data are used for analysis. Based on MCMC sampling, we estimate parameters of the model and efficiency index of each ports. Moreover, we add estimates from package Frontier 4.1c in order to compare them with Bayesian results.

A comparison study of various bivariate robust affine equivariant location estimators has been conducted under several contamination schemes. Also a new distance measure is introduced to define another kind of metrically trimmed mean(projection metrically trimmed mean) as a candidate for a robust location estimator. As criteria, dispersion measures suggested in Liu et al. (Ann. Statist. 27 (1999) 783–858), which are Scale, Bias and Bias+scale is used. The relative merits of each estimator are discussed.

Mark Weiser introduced ubiquitous computing in his article titled 'The computer for 21st Century' in 1991. This has been new paradigm after internet. Now, the rapid development of mobile computer, wireless network, and intelligent system has supported ubiquitous computing environment. In the related area of information science, the researchers have studied on ubiquitous computing. But in the field of Korea statistics, this research has not been worked yet. So, we proposed the connection between statistics and ubiquitous computing in this paper. As an example, we showed an efficient cache hoarding for ubiquitous computing using statistical methods. In experimental results, we verified our proposed issue.

We organize an array of vectors to guide calculation of the exact distribution of the K sample median test by a convolution formula. We obtain considerable savings in computation over earlier methods that enumerate all possible 2 x K tables with fixed margins. We also give a method of multiple comparisons using contrasts related to the statistic.

Abstract We consider the problem of computing sums of squares in multifactor analysis of variance models with unequal but nonzero numbers per cell. Commonly used computing methods for obtaining main-effect and interaction hypothesis mean squares solve linear model equations X′Xβ = X′Y. We present an algorithm based on Scheffé's method of contrasts that solves a smaller set of linear equations. It avoids subtraction of quadratic forms, which degrades floating-point accuracy and facilitates Scheffé's method of multiple comparisons. Selected efficiency calculations are presented. Key Words: ContrastsLagrange multiplierSparse matrixUnbalanced multifactor ANOVA algorithm