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Improved control chart for statistical process control using combined X and delayed EWMA statistics
Lim Johan,Lee Sungim 한국통계학회 2023 Journal of the Korean Statistical Society Vol.52 No.4
In this paper, we propose a new combination of X and EWMA charts in which we intentionally delay the EWMA statistic one lag to make it independent of X. The intentional delay of EWMA induces a power loss in detecting abnormality. However, owing to the independence of two control statistics, the new chart is much easier to set the in-control average run length at the aimed level than the existing combinations. We numerically show how simple to design the new chart and also show that the new chart performs as well as the existing charts in out-control average run length. Finally, we apply it to the cyber-attack detection problem from the NSL-KDD dataset.
Testing the effect of treatment on survival time with an immediate intermediate event
Lim, Johan,Lee, Sungim Taylor & Francis Inc. 2017 Communications in Statistics Vol.46 No.8
<P>In this paper, we consider testing the effects of treatment on survival time when a subject experiences an immediate intermediate event (IE) prior to death or predetermined endpoint. A two-stage model incorporating both (i) the effects of the covariates on the immediate IE and (ii)survival regression with the immediate IE and other covariates is presented. We study the likelihood ratio test (LRT) for testing the treatment effect based on the proposed two stage model. We propose two procedures: an asymptotic-based procedure and a resampling-based procedure, to approximate the null distribution of the LRT. We numerically show the advantages of the two stage modeling over the existing single stage survival model with interactions between the covariates and the immediate IE. In addition, an illustrative empirical example is provided.</P>
Detection of Differentially Expressed Gene Sets in a Partially Paired Microarray Data Set
Lim, Johan,Kim, Jayoun,Kim, Sang-cheol,Yu, Donghyeon,Kim, Kyunga,Kim, Byung Soo Walter de Gruyter GmbH 2012 Statistical applications in genetics and molecular Vol.11 No.3
<P>Partially paired data sets often occur in microarray experiments (Kim et al., 2005; Liu, Liang and Jang, 2006). Discussions of testing with partially paired data are found in the literature (Lin and Stivers 1974; Ekbohm, 1976; Bhoj, 1978). Bhoj (1978) initially proposed a test statistic that uses a convex combination of paired and unpaired t statistics. Kim et al. (2005) later proposed the t3 statistic, which is a linear combination of paired and unpaired t statistics, and then used it to detect differentially expressed (DE) genes in colorectal cancer (CRC) cDNA microarray data. In this paper, we extend Kim et al.'s t3 statistic to the Hotelling's T2 type statistic Tp for detecting DE gene sets of size p. We employ Efron's empirical null principle to incorporate inter-gene correlation in the estimation of the false discovery rate. Then, the proposed Tp statistic is applied to Kim et al's CRC data to detect the DE gene sets of sizes p=2 and p=3. Our results show that for small p, particularly for p=2 and marginally for p=3, the proposed Tp statistic compliments the univariate procedure by detecting additional DE genes that were undetected in the univariate test procedure. We also conduct a simulation study to demonstrate that Efron's empirical null principle is robust to the departure from the normal assumption.</P>
Statistical Properties of News Coverage Data
Lim, Eunju,Hahn, Kyu S.,Lim, Johan,Kim, Myungsuk,Park, Jeongyeon,Yoon, Jihee 한국통계학회 2012 Communications for statistical applications and me Vol.19 No.6
In the current analysis, we examine news coverage data widely used in media studies. News coverage data is usually time series data to capture the volume or the tone of the news media's coverage of a topic. We first describe the distributional properties of autoregressive conditionally heteroscadestic(ARCH) effects and compare two major American newspaper's coverage of U.S.-North Korea relations. Subsequently, we propose a change point detection model and apply it to the detection of major change points in the tone of American newspaper coverage of U.S.-North Korea relations.
Lee, Seul Ji,Yi, TacGhee,Ahn, Soo Hyun,Lim, Dong Kyu,Kim, Si-na,Lee, Hyun-Joo,Cho, Yun-Kyoung,Lim, Jae-Yol,Sung, Jong-Hyuk,Yun, Jeong-Ho,Lim, Johan,Song, Sun U.,Kwon, Sung Won Elsevier 2018 Analytica Chimica Acta Vol.1024 No.-
<P><B>Abstract</B></P> <P>Mesenchymal stem cells (MSCs) are a promising therapeutic option for cell-based therapy due to their immunomodulatory and regenerative properties. They can be isolated from various adult tissues, including bone marrow, fat, dental tissue, and glandular tissue. Although they share common characteristics, little is known about the biological differences between MSC populations derived from different tissues. In this study, we used MS to compare the endogenous metabolite level in the human MSCs originating from the bone marrow, adipose tissue, periodontal ligaments, and salivary glands. Using an optimized metabolomics technique, we verified that human MSCs exhibit differences in the endogenous metabolite level depending on their source material, while the multivariate analysis showed that 5 lysophosphatidylcholines and 3 lysophosphatidylethanolamines can serve as markers for the discrimination between MSC sources and may be related to differences in their differentiation capacity. These results may significantly contribute to further mechanistic studies on the MSCs and provide novel insights into the properties and optimal usage of MSCs from different tissues.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Endogenous metabolite level of human mesenchymal stem cells (MSCs) was evaluated. </LI> <LI> MSCs from different tissue sources were compared. </LI> <LI> Metabolic markers to distinguish MSCs by source tissue were identified. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Lim, Hong Euy,Lee, Moon-Soo,Ko, Young-Hoon,Park, Young-Min,Joe, Sook-Haeng,Kim, Yong-Ku,Han, Changsu,Lee, Hwa-Young,Pedersen, Susanne S,Denollet, Johan The Korean Academy of Medical Sciences 2011 JOURNAL OF KOREAN MEDICAL SCIENCE Vol.26 No.1
<P>This study aimed to develop a Korean version of the Type D Personality Scale-14 (DS14) and evaluate the psychiatric symptomatology of Korean cardiac patients with Type D personality. Healthy control (n = 954), patients with a coronary heart disease (n = 111) and patients with hypertension and no heart disease (n = 292) were recruited. All three groups completed DS14, the Eysenck Personality Questionnaire (EPQ), the state subscale of Spielberger State and Trait Anxiety Inventory (STAI-S), the Center for Epidemiologic Studies Short Depression Scale (CESD), and the General Health Questionnaire (GHQ). The Korean DS14 was internally consistent and stable over time. 27% of the subjects were classified as Type D. Type D individuals had significantly higher mean scores on the STAI-S, CESD, and GHQ compared to non-Type D subjects in each group. The Korean DS14 was a valid and reliable tool for identifying Type D personality. The general population and cardiovascular patients with Type D personality showed higher rate of depression, anxiety and psychological distress regarding their health. Therefore, identifying Type D personality is important in clinical research and practice in chronic medical disorders, especially cardiovascular disease, in Korea.</P>
Bias-reduced ℓ_1-trend filtering
Yu Donghyeon,Lim Johan,Son Won 한국통계학회 2023 Communications for statistical applications and me Vol.30 No.2
The ℓ_1-trend filtering method is one of the most widely used methods for extracting underlying trends from noisy observations. Contrary to the Hodrick-Prescott filtering, the ℓ_1-trend filtering gives piecewise linear trends. One of the advantages of the ℓ_1-trend filtering is that it can be used for identifying change points in piecewise linear trends. However, since the ℓ_1-trend filtering employs total variation as a penalty term, estimated piecewise linear trends tend to be biased. In this study, we demonstrate the biasedness of the ℓ_1-trend filtering in trend level estimation and propose a two-stage bias-reduction procedure. The newly suggested estimator is based on the estimated change points of the ℓ_1-trend filtering. Numerical examples illustrate that the proposed method yields less biased estimates for piecewise linear trends.