다차원 캔달의 타우의 통계학적 응용과 그의 해석

이우주,안재윤,Lee, Woojoo,Ahn, Jae Youn 한국통계학회 2013 응용통계연구 Vol.26 No.3

본 논문에서는 캔달의 타우(Kendall's tau)의 다차원으로의 확장과 그의 통계적 추론 및 해석에 대해 알아본다. 특히 다차원 캔달의 타우가 음의 값을 가질 때 의미를 해석하기 위해, 그것의 하한이 얻어지는 경우를 직관적으로 이해할 수 있도록 변수들간의 관계의 관점에서 설명하여본다. 또한 다차원 캔달의 타우를 실제 사례에 적용해 본후, 최근 Lee와 Ahn에서 연구된 d-countermonotonicity와 partially m-countermonotonic와 같은 새로운 개념을 통하여 캔달의 타우가 음의 값이 가질 때의 의미에 대해서 논의한다. We study multivariate extension of Kendall's tau and its statistical interpretation. There exist various versions of multivariate Kendall's tau, for example Scarsini (1984), Joe (1990) and Genest et al. (2011); however, few of them mention its lower bounds. For the bivariate case, the Fr$\acute{e}$chet-Hoeffding lower bound can achieve the lower bound of Kendall's tau. However in the multivariate case, the Fr$\acute{e}$chet-Hoeffding lower bound itself does not exist as a distribution, which makes the interpretation of Kendall's tau unclear when it has negative value. In this paper, we explain sufficient conditions to achieve the lower bound of Kendall's tau and provide real data examples that provide further insights into the interpretation for the lower bounds of Kendall's tau.

Seroprevalence of Measles IgG Antibodies in Married Immigrant Women from Multicultural Families in Korea

Lee Woojoo,Shin MI Yeong,Go Eunbyeul,Lim Hyun-Cheol,Jeon Ji-Yoon,Kwon Yerim,Lee Yerin,Kim Tong-Soo,Lee Sung-Keun,Bahk Young Yil 대한감염학회 2023 Infection and Chemotherapy Vol.55 No.3

Background: Although an effective vaccine has been available, measles still causes mast morbidity and mortality world widely. In Korea, a small number of measles cases have been reported through exposure to imported cases among young people with vaccine-induced measles immunity. Recently due to international migration including marriage, marriage migrants were the second-largest group of foreign population in Korea. Our study was carried out to obtain positive rate of measles antibody among married immigrant women from 12 countries in 10 Gun-Counties and 6 Cities, Korea. Materials and Methods: A total of 547 blood samples were collected from maternal multicultural members from 12 countries. The measles-specific IgG antibody was measured by ELISA (Enzyme-linked immunosorbent assay; Enzygnost® Anti-measles virus/IgG, Siemens Healthcare Diagnostics Products GmbH, Marburg, Germany). We performed a simple logistic regression to test whether the measles antibody seroprevalence differed by participant age, location, or country of birth and then calculated the likelihood ratio statistics to determine whether measles antibody seroprevalence differed by country of birth. Results: Overall positive measles seroprevalence was 75.3% (95% confidence interval: 71.7 – 78.9). Participants aged 20 - 24 years, 25 - 29 years, and 30 - 63 years has respective seropositivities of 52.5%, 55.3%, and 82.7%. In this study, the geometric mean titers of participants aged 21 - 29 years were slightly lower than those of participants aged over 30 years, which were 1,372 mIU/ml and 2,261 mIU/ml, respectively (average of total participants: 2,027 mIU/ml). Conclusion: The study provides detailed information about seroimmunity of the married immigrant population in Korea, which is important for measles elimination. Since the 1980s, most vaccine-preventable diseases including measles have been well-controlled. Nevertheless, sporadic measles outbreaks are still reported. Thus, special attention should be paid to the possible importation of infectious diseases such as measles by immigration.

Sparse Canonical Covariance Analysis for High-throughput Data

Lee, Woojoo,Lee, Donghwan,Lee, Youngjo,Pawitan, Yudi Walter de Gruyter GmbH 2011 Statistical applications in genetics and molecular Vol.10 No.1

<P>Canonical covariance analysis (CCA) has gained popularity as a method for the analysis of two sets of high-dimensional genomic data. However, it is often difficult to interpret the results because canonical vectors are linear combinations of all variables, and the coefficients are typically nonzero. Several sparse CCA methods have recently been proposed for reducing the number of nonzero coefficients, but these existing methods are not satisfactory because they still give too many nonzero coefficients. In this paper, we propose a new random-effect model approach for sparse CCA; the proposed algorithm can adapt arbitrary penalty functions to CCA without much computational demands. Through simulation studies, we compare various penalty functions in terms of the performance of correct model identification. We also develop an extension of sparse CCA to address more than two sets of variables on the same set of observations. We illustrate the method with an analysis of the NCI cancer dataset.</P>

TEI-power : Temperature Effect Inversion--Aware Dynamic Thermal Management

Lee, Woojoo,Han, Kyuseung,Wang, Yanzhi,Cui, Tiansong,Nazarian, Shahin,Pedram, Massoud Association for Computing Machinery 2017 Transactions on Design Automation of Electronic Sy Vol.22 No.3

<P>FinFETs have emerged as a promising replacement for planar CMOS devices in sub-20nm technology nodes. However, based on the temperature effect inversion (TEI) phenomenon observed in FinFET devices, the delay characteristics of FinFET circuits in sub-, near-, and superthreshold voltage regimes may be fundamentally different from those of CMOS circuits with nominal voltage operation. For example, FinFET circuits may run faster in higher temperatures. Therefore, the existing CMOS-based and TEI-unaware dynamic power and thermal management techniques would not be applicable. In this article, we present TEI-power, a dynamic voltage and frequency scaling-based dynamic thermal management technique that considers the TEI phenomenon and also the superlinear dependencies of power consumption components on the temperature and outlines a real-time trade-off between delay and power consumption as a function of the chip temperature to provide significant energy savings, with no performance penalty-namely, up to 42% energy savings for small circuits where the logic cell delay is dominant and up to 36% energy savings for larger circuits where the interconnect delay is considerable.</P>

Investigating dependence between frequency and severity via simple generalized linear models

Lee, Woojoo,Park, Sojung C.,Ahn, Jae Youn Elsevier 2019 Journal of the Korean Statistical Society Vol.48 No.1

<P><B>Abstract</B></P> <P>Recently, a body of literature proposed new models relaxing a widely-used but controversial assumption of independence between claim frequency and severity in non-life insurance rate making. This paper critically reviews a generalized linear model approach, where a dependence between claim frequency and severity is introduced by treating frequency as a covariate in a regression model for severity. As an extension of this approach, we propose a dispersion model for severity. For this model, the information loss caused by using average severity rather than individual severity is examined in detail and the parameter estimators suffering from low efficiency are identified. We also provide analytical solutions for the aggregate sum to help rate making. We show that the simple functional form used in current research may not properly reflect the real underlying dependence structure. A real data analysis is given to explain our analytical findings.</P>

Optimizing the Power Delivery Network in a Smartphone Platform

Woojoo Lee,Yanzhi Wang,Donghwa Shin,Chang, Naehyuck,Pedram, Massoud IEEE 2014 IEEE transactions on computer-aided design of inte Vol.33 No.1

<P>Smartphones consume a significant amount of power. Indeed, they can hardly provide a full day of use between charging operations even with a 2000 mAh battery. While power minimization and dynamic power management techniques have been heavily explored to improve the power efficiency of modules (processors, memory, display, GPS, etc.) inside a smartphone platform, there is one critical factor that is often overlooked: the power conversion efficiency of the power delivery network (PDN). This paper focuses on dc-dc converters, which play a pivotal role in the PDN of the smartphone platform. Starting from detailed models of the dc-dc converter designs, two optimization methods are presented: 1) static switch sizing to maximize the efficiency of a dc-dc converter under statistical loading profiles and 2) dynamic switch modulation to achieve the high efficiency enhancement under dynamically varying load conditions. To verify the efficacy of the optimization methods in actual smartphone platforms, this paper also presents a characterization procedure for the PDN. The procedure is as follows: 1) group the modules in the smartphone platform together and use profiling to estimate their average and peak power consumption levels and 2) build an equivalent dc-dc converter model for the power delivery path from the battery source to each group of modules and use linear regression to estimate the conversion efficiency of the corresponding equivalent converter. Experimental results demonstrate that the static switch sizing can achieve 6% power conversion efficiency enhancement, which translates to 19% reduction in power loss general usage of the smartphone. The dynamic switch modulation accomplishes similar improvement at the same condition, while also achieving high efficiency enhancement in various load conditions.</P>

Identifying and Assessing Interesting Subgroups in a Heterogeneous Population

Lee, Woojoo,Alexeyenko, Andrey,Pernemalm, Maria,Guegan, Justine,Dessen, Philippe,Lazar, Vladimir,Lehtiö,, Janne,Pawitan, Yudi Hindawi Publishing Corporation 2015 BioMed research international Vol.2015 No.-

<P>Biological heterogeneity is common in many diseases and it is often the reason for therapeutic failures. Thus, there is great interest in classifying a disease into subtypes that have clinical significance in terms of prognosis or therapy response. One of the most popular methods to uncover unrecognized subtypes is cluster analysis. However, classical clustering methods such as <I>k</I>-means clustering or hierarchical clustering are not guaranteed to produce clinically interesting subtypes. This could be because the main statistical variability—the basis of cluster generation—is dominated by genes not associated with the clinical phenotype of interest. Furthermore, a strong prognostic factor might be relevant for a certain subgroup but not for the whole population; thus an analysis of the whole sample may not reveal this prognostic factor. To address these problems we investigate methods to identify and assess clinically interesting subgroups in a heterogeneous population. The identification step uses a clustering algorithm and to assess significance we use a false discovery rate- (FDR-) based measure. Under the heterogeneity condition the standard FDR estimate is shown to overestimate the true FDR value, but this is remedied by an improved FDR estimation procedure. As illustrations, two real data examples from gene expression studies of lung cancer are provided.</P>

Application of Standardization for Causal Inference in Observational Studies: A Step-by-step Tutorial for Analysis Using R Software

Lee, Sangwon,Lee, Woojoo The Korean Society for Preventive Medicine 2022 예방의학회지 Vol.55 No.2

Epidemiological studies typically examine the causal effect of exposure on a health outcome. Standardization is one of the most straightforward methods for estimating causal estimands. However, compared to inverse probability weighting, there is a lack of user-centric explanations for implementing standardization to estimate causal estimands. This paper explains the standardization method using basic R functions only and how it is linked to the R package stdReg, which can be used to implement the same procedure. We provide a step-by-step tutorial for estimating causal risk differences, causal risk ratios, and causal odds ratios based on standardization. We also discuss how to carry out subgroup analysis in detail.

A reply to the letter on ‘Directed acyclic graphs for clinical research: a tutorial’

Woojoo Lee 대한내시경로봇외과학회 2023 Journal of Minimally Invasive Surgery Vol.26 No.4

A case study for intercontinental comparison of herd behavior in global stock markets

Lee, Woojoo,Choi, Yang Ho,Kim, Changki,Ahn, Jae Youn The Korean Statistical Society 2018 Communications for statistical applications and me Vol.25 No.2

Measuring market fear is an important way of understanding fundamental economic phenomena related to financial crises. There have been several approaches to measure market fear or panic level in a financial market. Recently, herd behavior has gained its popularity as important economic phenomena explaining the fear in the financial market. In this paper, we investigate herd behavior in global stock markets with a focus on intercontinental comparison. While various risk measures are available for the detection of herd behavior in the market, we use the standardized herd behavior index in Dhaene et al. (Insurance: Mathematics and Economics, 50, 357-370, 2012b) and Lee and Ahn (Dependence Modeling, 5, 316-329, 2017) for the comparison of herd behaviors in global stock markets. A global stock market data from Morgan Stanley Capital International is used to study herd behavior especially during periods of financial crises.