• Journal of the Korean Statistical Society
• /
• v.5 no.1
• /
• pp.35-48
• /
• 1976

Since the solution vector for input-output forecasting models is not directly obtainable, several iterative procedures have been proposed and utilized. As is often the case in numerical analysis, the question of the consistency between the original system and the converged system of the proposed iteration has been ignored, and no one has tried to express the converged solution explicitly. This paper examines this question and points out the inconsistencies between various well-known iterative procedures used to solve input-output models and the original input-output system.

• Journal of Applied Reliability
• /
• v.1 no.2
• /
• pp.175-181
• /
• 2001

Recently, the quality of software has become a major issue. The statistical models used in making predictions about the quality of software are termed software reliability growth models (SRGM). However, the existing SRGMs have not been satisfactory in predicting software reliability behavior (Keiller and Miller(1991), Keiller and Littlewood(1984), Musa(1987)). In this paper, we present a fuzzy-based interval estimation of software errors (failures).

• Communications for Statistical Applications and Methods
• /
• v.20 no.3
• /
• pp.199-205
• /
• 2013

We employ sieve bootstraps for empirical likelihood tests in time series models because their null distributions are often vulnerable to the presence of serial dependence. We found a significant size refinement of the bootstrapped versions of a Lagrangian Multiplier type test statistic regardless of the bandwidth choice required by long-run variance estimations.

• Journal of the Korean Statistical Society
• /
• v.34 no.4
• /
• pp.367-375
• /
• 2005

This paper presents an autocorrelation test that is applicable to dynamic panel data models with serially correlated errors. The residual-based GMM t-test is a significance test that is applied after estimating a dynamic model by using the instrumental variable (IV) method and is directly applicable to any other consistently estimated residuals. Monte Carlo simulations show that the t-test has considerably more power than the $m_2$ test or the Sargan test under both forms of serial correlation (i.e., AR(1) and MA(1)).

• Communications for Statistical Applications and Methods
• /
• v.6 no.3
• /
• pp.967-976
• /
• 1999

In this paper we study the Hellinger distance based goodness-of-fit tests that are analogs of likelihood ratio tests. The minimum Hellinger distance estimator (MHDE) in normal models provides an excellent robust alternative to the usual maximum likelihood estimator. Our simulation results show that the Hellinger deviance test (Simpson 1989) based goodness-of-fit test is robust when data contain outliers. The proposed hellinger deviance test(Simpson 1989) is a more direcct method for obtaining robust inferences than an automated outlier screen method used before the likelihood ratio test data analysis.

• Communications for Statistical Applications and Methods
• /
• v.11 no.3
• /
• pp.575-582
• /
• 2004

We explore the structure and usefulness of CERES plot as a basic tool for dealing with curvature as a function of the new predictor in generalized linear models. If a predictor has a nonlinear effect and there are nonlinear relationships among the predictors, the partial residual plot and augmented partial residual plot are not able to display the correct functional form of the predictor. Unlike these plots, the CERES plot can show the correct form. This is illustrated by simulated data.

• Communications for Statistical Applications and Methods
• /
• v.17 no.2
• /
• pp.205-222
• /
• 2010

A patent is a right of intellectual properties to an inventor or its assignee for a limited period under an international law. Not only in an invention of new machines, but it is competitive for using and creating technology in the world based on the patents. Most of the business models are good examples for patented technology, however a statistical analyzing model could be another one. In this paper we study and analyze the patents for the statistical analyzing and data mining models which are currently applied and registered, and suggest a statistical tool for analyzing and categorizing patent data. For this study all the patents in Korea and U.S. are listed and searched to sample the only cases concerning statistics.

• Journal of Advanced Research in Ocean Engineering
• /
• v.4 no.4
• /
• pp.185-194
• /
• 2018

In this study, the extreme sloshing pressure was predicted using various statistical models: three-parameter Weibull distribution, generalized Pareto distribution, generalized extreme value distribution, and three-parameter log-logistic distribution. The estimation of sloshing impact pressure is important in design of liquid cargo tank in severe sea state. In order to get the extreme values of local impact pressures, a lot of model tests have been carried out and statistical analysis has been performed. Three-parameter Weibull distribution and generalized Pareto distribution are widely used as the statistical analysis method in sloshing phenomenon, but generalized extreme value distribution and three-parameter log-logistic distribution are added in this study. Additionally, statistical distributions are fitted to peak pressure data using three different parameter estimation methods. The data were obtained from a three-dimensional sloshing model text conducted at Seoul National University. The loading conditions were 20%, 50%, and 95% of tank height, and the analysis was performed based on the measured impact pressure on four significant panels with large sloshing impacts. These fittings were compared by observing probability of exceedance diagrams and probability plot correlation coefficient test for goodness-of-fit.

• Journal of the Korean Statistical Society
• /
• v.33 no.1
• /
• pp.25-34
• /
• 2004

In this paper we propose a prediction method on the regression model with randomly censored observations of the training data set. The least squares support vector machine regression is applied for the regression function prediction by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed prediction method.

• Asian-Australasian Journal of Animal Sciences
• /
• v.14 no.7
• /
• pp.910-914
• /
• 2001

The teat number of a sow plays an important role for weaning pigs and has been utilized in selection of swine breeding stock. Various linear models have been employed for genetic analyses of teat number although the teat number can be considered as a count trait. Theoretically, Poisson error mixed models are more appropriate for count traits than Normal error mixed models. In this study, the two models were compared by analyzing data simulated with Poisson error. Considering the mean square errors and correlation coefficients between observed and fitted values, the Poisson generalized linear mixed model (PGLMM) fit the data better than the Normal error mixed model. Also these two models were applied to analyzing teat numbers in four breeds of swine (Landrace, Yorkshire, crossbred of Landrace and Yorkshire, crossbred of Landrace, Yorkshire, and Chinese indigenous Min pig) collected in China. However, when analyzed with the field data, the Normal error mixed model, on the contrary, fit better for all the breeds than the PGLMM. The results from both simulated and field data indicate that teat numbers of swine might not have variance equal to mean and thus not have a Poisson distribution.