• Journal of the Korean Statistical Society
• /
• v.28 no.1
• /
• pp.107-124
• /
• 1999

An approximate Bayes criterion for multivariate Behrens-Fisher problem is proposed and examined. Development of the criterion involves derivation of approximate Bayes factor using the imaginary training sample approach introduced by Speigelhalter and Smith (1982). The criterion is designed to develop a Bayesian test, so that it provides an alternative test to other tests based upon asymptotic sampling theory (such as the tests suggested by Bennett(1951), James(1954) and Yao(1965). For the derived criterion, numerical studies demonstrate routine application and give comparisons with the classical tests.

• Communications for Statistical Applications and Methods
• /
• v.2 no.2
• /
• pp.303-309
• /
• 1995

Many measures to detect multicollinearity in linear regression have been proposed in statistics and numerical analysis literature. Among them, condition number and variance inflation factor(VIF) are most popular. In this study, we give new interpretations of condition number and VIF in linear regression, using geometry on the explanatory space. In the same line, we derive natural measures of condition number and VIF for logistic regression. These computer intensive measures can be easily extended to evaluate multicollinearity in generalized linear models.

• The Korean Journal of Applied Statistics
• /
• v.25 no.4
• /
• pp.553-561
• /
• 2012

Various statistical models to Estimate GDP (measured as a nation's economic situation) have been developed. In this paper an autoregressive distributed lag model, factor model, and a Bayesian VAR model estimate quarterly GDP as a single model; the combined estimates were evaluated to compare a single model. Subsequently, we suggest that some combined models are better than a single model to estimate quarterly GDP.

• Journal of the Korean Statistical Society
• /
• v.27 no.1
• /
• pp.11-23
• /
• 1998

A Bayes criterion for testing the equality of covariance matrices of two multivariate normal distributions is proposed and studied. Development of the criterion invloves calculation of Bayes factor using the imaginary sample method introduced by Spiegelhalter and Smith (1982). The criterion is designed to develop a Bayesian test criterion, so that it provides an alternative test criterion to those based upon asymptotic sampling theory (such as Box's M test criterion). For the constructed criterion, numerical studies demonstrate routine application and give comparisons with the traditional test criteria.

• Proceedings of the Safety Management and Science Conference
• /
• 2003.11a
• /
• pp.75-80
• /
• 2003

The objectives of this study is to achieved by the use of the conceptual approach and accident data bases to develop statistical accident analysis, effectiveness values, comparison analysis of statistical models to determine which variables are significantly related to accidents, human factor, and hazard factor analysis, all of which were used in the railroad crossing. The result from this approach applicable to the railroad crossing where systematic safety management criteria have been considered.

• Journal of the Korean Statistical Society
• /
• v.22 no.1
• /
• pp.39-54
• /
• 1993

In the three-factor nested variance component model with equal numbers in the cells given by $y_{ijkm} = \mu + A_i + B_{ij} + C_{ijk} + \varepsilon_{ijkm}$, the exact confidence intervals of the variance component of $\sigma^2_A, \sigma^2_B, \sigma^2_C, \sigma^2_{\varepsilon}, \sigma^2_A/\sigma^2_{\varepsilon}, \sigma^2_B/\sigma^2_{\varepsilon}, \sigma^2_C/\sigma^2_{\varepsilon}, \sigma^2_A/\sigma^2_C, \sigma^2_B/\sigma^2_C$ and $\sigma^2_A/\sigma^2_B$ are not found out yet. In this paper approximate lower and upper confidence intervals are presented.

• Journal of the Korean Statistical Society
• /
• v.15 no.1
• /
• pp.31-45
• /
• 1986

It is often necessary to obtain some run orders in factorial designs which have a small number of factor level changes and a small linear time trend. In this paper we propose an algorithm to find optimal or near-optimal run orders for $2^4, 2^5, 3^2$ and $2\cdot 3^2$ factorial designs under the criterion that the number of factor level changes and the linear time trend should be simultaneously small.

• Journal of the Korean Statistical Society
• /
• v.29 no.1
• /
• pp.81-94
• /
• 2000

For the balanced variance component model, sometimes intraclass correlation coefficient is of interest. If there is little information about the parameter, then the reference prior(Berger and Bernardo, 1992) is widely used. Pettit nd Young(1990) considered a measrue of the effect of a single observation on a logarithmic Bayes factor. However, under such a reference prior, the Bayes factor depends on the ratio of unspecified constants. In order to discard this problem, influence diagnostic measures using the intrinsic Bayes factor(Berger and Pericchi, 1996) is presented. Finally, one simulated dataset is provided which illustrates the methodology with appropriate simulation based computational formulas. In order to overcome the difficult Bayesian computation, MCMC methods, such as Gibbs sampler(Gelfand and Smith, 1990) and Metropolis algorithm, are empolyed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.824-828
• /
• 2007

In this paper Statistical Energy Analysis has been considered to predict middle, high frequency air borne interior noise. PIM method is used for verification. Damping loss factor and coupling loss factor have been derived from the response(SPL) of sub systems when the power is applied. The airborne SEA model of vehicle is modeled through AutoSea2. Insulation material's absorption coefficient and transmission loss are acquired from closed form solution and experiment.

• Journal of the Korean Statistical Society
• /
• v.29 no.3
• /
• pp.337-350
• /
• 2000

This paper suggests a new diagnostic measure and a stopping rule for detecting influential observations in multiple discriminant analysis (MDA). It is developed from a Bayesian point of view using a default Bayes factor obtained from the fractional Bayes factor methodology. The Bayes factor is taken as a discriminatory information in MDA. It is shown that the effect of an observation over the discriminatory information is fully explained by the diagnostic measure. Based on the measure, we suggest a stopping rule for detecting influential observations in a given training sample. As a tool for interpreting the measure a graphical method is sued. Performance of the method is used. Performance of the method is examined through two illustrative examples.