• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.687-696
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• 2013

The detection and the examination of outliers are important parts of data analysis because some outliers in the data may have a detrimental effect on statistical analysis. Outlier detection methods have been discussed by many authors. In this article, we propose to apply Hadi and Simonoff's (1993) method to penalized spline a regression model to detect multiple outliers. Simulated data sets and real data sets are used to illustrate and compare the proposed procedure to a penalized spline regression and a robust penalized spline regression.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.71-79
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• 2013

Semiparametric and nonparametric small area estimations have been studied to overcome a large variance due to a small sample size allocated in a small area. In this study, we investigate semiparametric and nonparametric mixed effect small area estimators using penalized spline and kernel smoothing methods respectively and compare their performances using labor statistics.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.261-267
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• 2012

In constructing a credit scorecard, each characteristic variable is divided into a few attributes; subsequently, weights are assigned to those attributes in a process called coarse classification. While partitioning a characteristic variable into attributes, one should determine appropriate cutpoints for the partition. In this paper, we propose a cutpoint selection method via penalization. In addition, we compare the performances of the proposed method with classification spline machine (Koo et al., 2009) on both simulated and real credit data.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1295-1309
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• 2016

This paper is to reproduce the result of Kim et al. (2014) by deriving a benchmark dose lower bound (BMDL) of lead based on the 2005 cohort data set of Children's Health and Environmental Research (CHEER) data set. The ADHD rating scales in the 2005 cohort were not consistent along the three follow-ups since two different ADHD rating scales were used in the cohort. We first unified the ADHD rating scales in the 2005 cohort by deriving a conversion formula using a penalized linear spline. We then constructed two linear mixed models for the 2005 cohort which reflected the longitudinal characteristics of the data set. The first model introduced the random intercept and the random slope terms and the second model assumed the first order autoregressive structure of the error term. Using these two models, we derived the BMDLs of lead and reconfirmed the "regression to the mean" nature of the ADHD score discovered by Kim et al. (2014). We also noticed that there was a definite difference between the sampling distributions of the two cohorts. As a result, taking this difference into account, we were able to obtain the consistent result with Kim et al. (2014).

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.211-220
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• 2015

Recently, model-based iterative reconstruction (MBIR) has played an important role in transmission tomography by significantly improving the quality of reconstructed images for low-dose scans. MBIR is based on the penalized-likelihood (PL) approach, where the penalty term (also known as the regularizer) stabilizes the unstable likelihood term, thereby suppressing the noise. In this work we further improve MBIR by using a more expressive regularizer which can restore the underlying image more accurately. Here we used a spline regularizer derived from a linear combination of the two-dimensional splines with first- and second-order spatial derivatives and applied it to a non-quadratic convex penalty function. To derive a PL algorithm with the spline regularizer, we used a separable paraboloidal surrogates algorithm for convex optimization. The experimental results demonstrate that our regularization method improves reconstruction accuracy in terms of both regional percentage error and contrast recovery coefficient by restoring smooth edges as well as sharp edges more accurately.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.13-28
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• 2018

The ministry of Environment of Korea initiated two follow-up surveys in 2005 and 2006 to investigate environmental effect on children's health. These two cohorts, referred to as the 2005 Cohort and 2006 Cohort, were followed up three times every two years. This data set was referred to as the Children's Health and Environmental Research (CHEER) data set. This paper reproduces the existing research results of Kim et al. (Journal of the Korean Data and Information Science Society, 25, 987-998, 2014) and Lee et al. (The Korean Journal of Applied Statistics, 29, 1295-1310, 2016) and derive a benchmark dose lower limit (BMDL) for blood lead level for attention deficit hyperactivity disorder (ADHD) after pooling two cohort data sets. The different ADHD rating scales were unified by applying the conversion formula proposed by Lee et al. (2016). The random effect model and AR(1) model were built to reflect the longitudinal characteristics and regression to the mean phenomenon. Based on these models the BMDLs for blood lead levels were derived using the BMDL formula and the simulation. We obtained a hight level of BMDLs when we pooled two independent cohort data sets.