• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.1-12
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• 2024

In many real-world data, multiple response variables are often dependent on the same set of explanatory variables. In particular, if several response variables are correlated with each other, simultaneous estimation considering the correlation between response variables might be more effective way than individual analysis by each response variable. In this multivariate regression analysis, least distance estimator (LDE) can estimate the regression coefficients simultaneously to minimize the distance between each training data and the estimates in a multidimensional Euclidean space. It provides a robustness for the outliers as well. In this paper, we examine the least distance estimation method in multivariate linear regression analysis, and furthermore, we present the penalized least distance estimator (PLDE) for efficient variable selection. The LDE technique applied with the adaptive group LASSO penalty term (AGLDE) is proposed in this study which can reflect the correlation between response variables in the model and can efficiently select variables according to the importance of explanatory variables. The validity of the proposed method was confirmed through simulations and real data analysis.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.961-975
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• 2016

The hierarchically penalized support vector machine (H-SVM) has been developed to perform simultaneous classification and input variable selection when input variables are naturally grouped or generated by factors. However, the H-SVM may suffer from estimation inefficiency because it applies the same amount of shrinkage to each variable without assessing its relative importance. In addition, when analyzing imbalanced data with uneven class sizes, the classification accuracy of the H-SVM may drop significantly in predicting minority class because its classifiers are undesirably biased toward the majority class. To remedy such problems, we propose the weighted adaptive H-SVM (WAH-SVM) method, which uses a adaptive tuning parameters to improve the performance of variable selection and the weights to differentiate the misclassification of data points between classes. Numerical results are presented to demonstrate the competitive performance of the proposed WAH-SVM over existing SVM methods.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.715-723
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• 2002

This paper is to show a clustering application of a density estimation method that utilizes the Gaussian mixture model. We define "closeness measure" as a clustering criterion to see how close given two Gaussian components are. Closeness measure is defined as the ratio of log likelihood between two Gaussian components. According to simulations using artificial data, the clustering algorithm turned out to be very powerful in that it can correctly determine clusters in complex situations, and very flexible in that it can produce different sizes of clusters based on different threshold valuesold values

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.677-684
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• 2014

Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. To improve the estimation performance of SVQR we propose to use SVQR ensemble with bagging (bootstrap aggregating), in which SVQRs are trained independently using the training data sets sampled randomly via a bootstrap method. Then, they are aggregated to obtain the estimator of the quantile regression function using the penalized objective function composed of check functions. Experimental results are then presented, which illustrate the performance of SVQR ensemble with bagging.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.349-359
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• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.265-282
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• 2024

Research in the area of financial portfolio optimization, with the dual goals of increasing expected returns and reducing financial risk, has actively explored various risk measurement indicators. At the same time, the incorporation of various penalty terms to construct efficient portfolios with limited assets has been investigated. In this study, we present a novel portfolio optimization formula that combines the mean-shortfall portfolio and the SLOPE penalty term. Specifically, we formulate this optimization expression, which differs from linear programming, by introducing new variables and using the alternating direction method of multipliers (ADMM) algorithms. Through simulations, we validate the automatic grouping property of the SLOPE penalty term within the proposed mean-shortfall portfolio. Furthermore, using the model introduced in this paper, we propose and evaluate four different types of portfolio compositions relevant to real-world investment scenarios through empirical data analysis.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.543-559
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• 2019

Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.

• Journal of Korean Neurosurgical Society
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• v.66 no.5
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• pp.536-542
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• 2023

Objective : Surgical site infection is the most detrimental complication following cranioplasty. In other surgical fields, intrawound vancomycin powder application has been introduced to prevent surgical site infection and is widely used based on results in multiple studies. This study evaluated the effect of intrawound vancomycin powder in cranioplasty compared with the conventional method without topical antibiotics. Methods : This retrospective study included 580 patients with skull defects who underwent cranioplasty between August 1, 1998 and December 31, 2021. The conventional method was used in 475 (81.9%; conventional group) and vancomycin powder (1 g) was applied on the dura mater and bone flap in 105 patients (18.1%; vancomycin powder group). Surgical site infection was defined as infection of the incision, organ, or space that occurred after cranioplasty. Surgical site infection within 1-year surveillance period was compared between the conventional and vancomycin powder groups with logistic regression analysis. Penalized likelihood estimation method was used in logistic regression to deal with zero events. All local and systemic adverse events associated with topical vancomycin application were also evaluated. Results : Surgical site infection occurred in 31 patients (5.3%) and all were observed in the conventional group. The median time between cranioplasty and detection of surgical site infection was 13 days (range, 4-333). Staphylococci were the most common organisms and identified in 25 (80.6%) of 31 cases with surgical site infections. The surgical site infection rate in the vancomycin powder group (0/105, 0.0%) was significantly lower than that in the conventional group (31/475, 6.5%; crude odds ratio [OR], 0.067; 95% confidence interval [CI], 0.006-0.762; adjusted OR, 0.068; 95% CI, 0.006-0.731; p=0.026). No adverse events associated with intrawound vancomycin powder were observed during the follow-up. Conclusion : Intrawound vancomycin powder effectively prevented surgical site infections following cranioplasty without local or systemic adverse events. Our results suggest that intrawound vancomycin powder is an effective and safe strategy for patients undergoing cranioplasty.