• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.553-562
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• 2021

Directional regression (DR; Li and Wang, 2007) is well-known as an exhaustive sufficient dimension reduction method, and performs well in complex regression models to have linear and nonlinear trends. However, the extension of DR is not well-done upto date, so we will extend DR to accommodate multivariate regression and large p-small n regression. We propose three versions of DR for multivariate regression and discuss how DR is applicable for the latter regression case. Numerical studies confirm that DR is robust to the number of clusters and the choice of hierarchical-clustering or pooled DR.

• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.169-174
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• 2015

Permutation p-values are provided for the agreement measures for multivariate interval data among many raters. Three agreement measures, Berry and Mielke's measure, Janson and Olsson's measure, and Um's measure are described and compared. Exact and resampling permutation methods are utilized to compute p-values and empirical quantile limits for three measures. Comparisons of p-values demonstrate that resampling permutation methods provide close approximations to exact p-values, and Berry and Mielke's measure and Um's measure show similar performance in terms of measuring agreement.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.875-883
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• 2001

We derive the asymptotic relative efficiencies in two special cases of Chaudhuri's estimators for the multivariate one sample problem. And we compare those two when observations are independent and identically distributed from a family of spherically symmetric distributions including normal distributions.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.591-597
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• 1998

We prove (at least empirically) that some forms of the scaled residuals calculated from i.i.d. multivariate normal random vectors are ancillary. We further show that, if the scaled residuals are ancillary, then they have the same distribution whatever form of rotation is rosed to remove sample correlations.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.189-196
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• 2004

The sensitivity of the multivariate Box-Cox transformation model to simultaneous perturbations of all the data based on the likelihood displacement is studied in order to detect outliers. An example is given for illustration.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.243-248
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• 1995

The problem of optimal design for a nonparametric regression with binary data is considered. The aim of the statistical analysis is the estimation of a quantal response surface in two dimensions. Bias, variance and IMSE of kernel estimates are derived. The optimal design density with respect to asymptotic IMSE is constructed.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.33-40
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• 1994

The conditional expectation of a random variable in a multivariate normal random vector is a multiple linear regression on its predecessors. Using this fact, the least median of squares estimation method developed in a multiple linear regression is adapted to a multivariate data to identify influential observations. The resulting method clearly detect outliers and it avoids the masking effect.

• Journal of Korean Society of Rural Planning
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• v.11 no.2 s.27
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• pp.35-41
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• 2005

It is necessary that the basic concept of rural planning update from economics based on the production and sale into experience of natural resources and traditional culture. For the purpose of set up development direction for rural district, it is requisite to the multivariate analysis. In this study, the methods of the classification of rural village with existing data are studied, the results looking for applying to the making of principal viewpoint of the development. The analysis methods of classification are used the PCA, CA and combination of these, and making the revised method for localization of the rural district. In this study, we implement classification of regional pattern analysis for the planning of rural district in Chungbuk province.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.919-929
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• 2013

We compared two basic methods, combine-accumulate method and accumulate-combine method, using the past quality information in multivariate quality control procedure for monitoring mean vector of multivariate normal process. When small or moderate shifts have occurred, accumulate-combine method yields smaller average run length (ARL) and average time to signal (ATS) than combine-accumulate method. On the other hand, we have found from our numerical results that combine-accumulate method has better performances in terms of switching behavior than accumulate-combine method. In industry, a quality engineer could select one of the two method under the comprehensive consideration about the required time to signal, switching behavior, and other physical factors in the production process.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.605-618
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• 2018

Risk management has been a crucial part of the daily operations of the financial industry over the past two decades. Value at Risk (VaR), a quantitative measure introduced by JP Morgan in 1995, is the most popular and simplest quantitative measure of risk. VaR has been widely applied to the risk evaluation over all types of financial activities, including portfolio management and asset allocation. This paper uses the implementations of multivariate GARCH models and copula methods to illustrate the performance of a one-day-ahead VaR prediction modeling process for high-dimensional portfolios. Many factors, such as the interaction among included assets, are included in the modeling process. Additionally, empirical data analyses and backtesting results are demonstrated through a rolling analysis, which help capture the instability of parameter estimates. We find that our way of modeling is relatively robust and flexible.