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Prof. Jong-June Jeon
University of Seoul

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Research Keywords & Expertise

0 Machine learing
0 high dimensional data analysis
0 Hydrology and Water Resource Management
0 ranking analysis
0 Statistcal modeling

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high dimensional data analysis

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Articles
Published: 07 May 2021 in Journal of Computational and Graphical Statistics
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We present a neural network model for estimation of multiple conditional quantiles that satisfies the non-crossing property. Motivated by linear non-crossing quantile regression, we propose a non-crossing quantile neural network model with inequality constraints. In particular, to use the first-order optimization method, we develop a new algorithm for fitting the proposed model. This algorithm gives a nearly optimal solution without the projected gradient step that requires polynomial computation time. We compare the performance of our proposed model with that of existing neural network models on simulated and real precipitation data.

ACS Style

Sang Jun Moon; Jong-June Jeon; Jason Sang Hun Lee; Yongdai Kim. Learning Multiple Quantiles With Neural Networks. Journal of Computational and Graphical Statistics 2021, 1 -11.

AMA Style

Sang Jun Moon, Jong-June Jeon, Jason Sang Hun Lee, Yongdai Kim. Learning Multiple Quantiles With Neural Networks. Journal of Computational and Graphical Statistics. 2021; ():1-11.

Chicago/Turabian Style

Sang Jun Moon; Jong-June Jeon; Jason Sang Hun Lee; Yongdai Kim. 2021. "Learning Multiple Quantiles With Neural Networks." Journal of Computational and Graphical Statistics , no. : 1-11.

Journal article
Published: 22 February 2021 in Sustainability
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Statistical models that can generate a road-traffic noise map for a city or area where only elementary urban design factors are determined, and where no concrete urban morphology, including buildings and roads, is given, can provide basic but essential information for developing a quiet and sustainable city. Long-term cost-effective measures for a quiet urban area can be considered at early city planning stages by using the statistical road-traffic noise map. An artificial neural network (ANN) and an ordinary least squares (OLS) model were developed by utilizing data on urban form indicators, based on a 3D urban model and road-traffic noise levels from a normal noise map of city A (Gwangju). The developed ANN and OLS models were applied to city B (Cheongju), and the resultant statistical noise map of city B was compared to an existing normal road-traffic noise map of city B. The urban form indicators that showed multi-collinearity were excluded by the OLS model, and among the remaining urban forms, road-related urban form indicators such as traffic volume and road area density were found to be important variables to predict the road-traffic noise level and to design a quiet city. Comparisons of the statistical ANN and OLS noise maps with the normal noise map showed that the OLS model tends to under-estimate road-traffic noise levels, and the ANN model tends to over-estimate them.

ACS Style

Phillip Kim; Hunjae Ryu; Jong-June Jeon; Seo Chang. Statistical Road-Traffic Noise Mapping based on Elementary Urban Forms in Two Cities of South Korea. Sustainability 2021, 13, 2365 .

AMA Style

Phillip Kim, Hunjae Ryu, Jong-June Jeon, Seo Chang. Statistical Road-Traffic Noise Mapping based on Elementary Urban Forms in Two Cities of South Korea. Sustainability. 2021; 13 (4):2365.

Chicago/Turabian Style

Phillip Kim; Hunjae Ryu; Jong-June Jeon; Seo Chang. 2021. "Statistical Road-Traffic Noise Mapping based on Elementary Urban Forms in Two Cities of South Korea." Sustainability 13, no. 4: 2365.

Journal article
Published: 20 September 2020 in Applied Sciences
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In the field of speaker verification, probabilistic linear discriminant analysis (PLDA) is the dominant method for back-end scoring. To estimate the PLDA model, the between-class covariance and within-class precision matrices must be estimated from samples. However, the empirical covariance/precision estimated from samples has estimation errors due to the limited number of samples available. In this paper, we propose a method to improve the conventional PLDA by estimating the PLDA model using the regularized within-class precision matrix. We use graphical least absolute shrinking and selection operator (GLASSO) for the regularization. The GLASSO regularization decreases the estimation errors in the empirical precision matrix by making the precision matrix sparse, which corresponds to the reflection of the conditional independence structure. The experimental results on text-dependent speaker verification reveal that the proposed method reduce the relative equal error rate by up to 23% compared with the conventional PLDA.

ACS Style

Sung-Hyun Yoon; Jong-June Jeon; Ha-Jin Yu. Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification. Applied Sciences 2020, 10, 6571 .

AMA Style

Sung-Hyun Yoon, Jong-June Jeon, Ha-Jin Yu. Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification. Applied Sciences. 2020; 10 (18):6571.

Chicago/Turabian Style

Sung-Hyun Yoon; Jong-June Jeon; Ha-Jin Yu. 2020. "Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification." Applied Sciences 10, no. 18: 6571.

Hollow organ gi
Published: 10 September 2020 in Abdominal Radiology
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Differentiating complicated appendicitis has become important, as multiple trials showed that non-operative management of uncomplicated appendicitis is feasible. We developed and validated a diagnostic model to differentiate complicated from uncomplicated appendicitis. This retrospective study included 1153 patients (mean age ± standard deviation, 30 ± 8 years) with appendicitis on CT (804 patients for development, and 349 for validation). Complicated appendicitis was confirmed in 300 and 121 patients in the development and validation datasets, respectively. The reference standard was surgical or pathological report except in 7 patients who underwent percutaneous abscess drainage. We developed a model using multivariable logistic regression and Bayesian information criterion. We assessed calibration and discriminatory performance of the model in the validation dataset via calibration plot and the area under the curve (AUC), respectively. We measured sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and proportion of false- and true-negatives of the model in the validation dataset, targeting 95% sensitivity. Five CT features (contrast-enhancement defect of the appendiceal wall, abscess, moderate or severe periappendiceal fat stranding, appendiceal diameter, and extraluminal air) and percentage of segmented neutrophil were included in our model. The calibration slope was 1.03, and AUC was 0.81 (95% CI 0.77–0.85) in the validation dataset. The sensitivity, specificity, PPV, NPV, and proportion of false- and true-negatives were 93.4% (91.8–99.1), 28.1% (13.6–24.1), 40.8% (35.0–46.8), 88.9% (79.3–95.1), 2.3%, and 18.3%, respectively. Our model may identify patients with unequivocally uncomplicated appendicitis, who may benefit from non-operative management with low risk of failure.

ACS Style

Hae Young Kim; Ji Hoon Park; Sung Soo Lee; Jong-June Jeon; Chang Jin Yoon; Kyoung Ho Lee. Differentiation between complicated and uncomplicated appendicitis: diagnostic model development and validation study. Abdominal Radiology 2020, 46, 948 -959.

AMA Style

Hae Young Kim, Ji Hoon Park, Sung Soo Lee, Jong-June Jeon, Chang Jin Yoon, Kyoung Ho Lee. Differentiation between complicated and uncomplicated appendicitis: diagnostic model development and validation study. Abdominal Radiology. 2020; 46 (3):948-959.

Chicago/Turabian Style

Hae Young Kim; Ji Hoon Park; Sung Soo Lee; Jong-June Jeon; Chang Jin Yoon; Kyoung Ho Lee. 2020. "Differentiation between complicated and uncomplicated appendicitis: diagnostic model development and validation study." Abdominal Radiology 46, no. 3: 948-959.

Water resources and hydrologic engineering
Published: 14 July 2020 in KSCE Journal of Civil Engineering
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Although many studies have sought to characterize future meteorological droughts, a few efforts have been done for quantifying the uncertainty, inter-model variability, arises from global circulation models (GCM) ensemble. A clear understanding of the uncertainty in multiple GCMs should be preceded before future meteorological droughts are projected. Therefore, this study evaluates the uncertainty in future meteorological drought characteristics that are induced by GCM ensemble using the custom measure “the degree of GCM spreading”. Future meteorological drought indices, the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), were computed to five different time scales: 3, 6, 9, 12 and 24 months using statistically downscaled 28 GCMs under Representative Concentration Pathway (RCP) 4.5 and 8.5 at 60 weather stations in South Korea. The frequency, duration, and severity of drought events were estimated for three different future periods; F1 (2010–2039), F2 (2040–2069), and F3 (2070–2099). It was found that the uncertainty increases as the time scale lengthens regardless of a choice of drought indices or RCP scenarios. It also turned out that the SPI exhibits larger uncertainty rather than the SPEI, because temperature data exhibit a relatively much smaller variability comparing to precipitation data. Moreover, there was a shift of regions having larger values of the increasing rate between F1 and F2, which is shift from the north-western to southern region of South Korea.

ACS Style

Jang Hyun Sung; Junehyeong Park; Jong-June Jeon; Seung Beom Seo. Assessment of Inter-Model Variability in Meteorological Drought Characteristics Using CMIP5 GCMs over South Korea. KSCE Journal of Civil Engineering 2020, 24, 2824 -2834.

AMA Style

Jang Hyun Sung, Junehyeong Park, Jong-June Jeon, Seung Beom Seo. Assessment of Inter-Model Variability in Meteorological Drought Characteristics Using CMIP5 GCMs over South Korea. KSCE Journal of Civil Engineering. 2020; 24 (9):2824-2834.

Chicago/Turabian Style

Jang Hyun Sung; Junehyeong Park; Jong-June Jeon; Seung Beom Seo. 2020. "Assessment of Inter-Model Variability in Meteorological Drought Characteristics Using CMIP5 GCMs over South Korea." KSCE Journal of Civil Engineering 24, no. 9: 2824-2834.

Journal article
Published: 20 March 2020 in Computational Statistics & Data Analysis
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We consider the LASSO estimator for compositional data in which covariates are nonnegative, and their sum is always one. Due to the linear constraint of the regression coefficients caused by the sum to one condition, standard algorithms for LASSO cannot be applied directly to compositional data. Hence, a specific regularized regression model with linear constraints is commonly used. However, linear constraints incur additional computational time, which becomes severe in high-dimensional cases. Additionally, the exact computation for the regression is not investigated under existing methods. In this paper, we first propose an exact solution path algorithm for a l1 regularized regression with high-dimensional compositional data and extend to a classification model. We also compare its computational speed with that of previously developed algorithms and then apply the proposed algorithm to analyzing income inequality data in economics and human gut microbiome data in biology. By analyzing simulated and real data sets, we illustrate that our specialized algorithm is significantly more efficient than the generalized LASSO algorithm for compositional data.

ACS Style

Jong-June Jeon; Yongdai Kim; Sungho Won; Hosik Choi. Primal path algorithm for compositional data analysis. Computational Statistics & Data Analysis 2020, 148, 106958 .

AMA Style

Jong-June Jeon, Yongdai Kim, Sungho Won, Hosik Choi. Primal path algorithm for compositional data analysis. Computational Statistics & Data Analysis. 2020; 148 ():106958.

Chicago/Turabian Style

Jong-June Jeon; Yongdai Kim; Sungho Won; Hosik Choi. 2020. "Primal path algorithm for compositional data analysis." Computational Statistics & Data Analysis 148, no. : 106958.

Journal article
Published: 30 August 2019 in Journal of Hydrology
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This study proposes a Bayesian model for the nonstationary generalized extreme value (GEV) distributions with abrupt changes of location parameters and smooth change of scale parameters. Our motivation is that the quantiles of hydrological process depend on scale parameter as well as location parameter in the GEV distribution. The proposed model extends the nonstationary Bayesian model with jumping location parameters on the time domain, and it provides a wider class of models to explain the mixing effect of abrupt location changes and smooth dispersion changes in the hydrological processes. This study also suggests the use of the Bayesian model selection procedure by logarithm of the pseudo marginal likelihood (LPML). Numerical study reveals that the proposed method can provide viable estimates of return levels through model selection. We apply the proposed method to analyze annual maximum precipitation acquired from the Korea Meteorological Administration.

ACS Style

Gwangsu Kim; Jong-June Jeon. Bayesian model for hydrological processes with jumping location and varying dispersion. Journal of Hydrology 2019, 578, 124087 .

AMA Style

Gwangsu Kim, Jong-June Jeon. Bayesian model for hydrological processes with jumping location and varying dispersion. Journal of Hydrology. 2019; 578 ():124087.

Chicago/Turabian Style

Gwangsu Kim; Jong-June Jeon. 2019. "Bayesian model for hydrological processes with jumping location and varying dispersion." Journal of Hydrology 578, no. : 124087.

Journal article
Published: 03 April 2019 in Sustainability
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The numerous choices between climate change scenarios makes decision-making difficult for the assessment of climate change impacts. Previous studies have used climate models to compare performance in terms of simulating observed climates or preserving model variability among scenarios. In this study, the Katsavounidis-Kuo-Zhang algorithm was applied to select representative climate change scenarios (RCCS) that preserve the variability among all climate change scenarios (CCS). The performance of multi-model ensemble of RCCS was evaluated for reference and future climates. It was found that RCCS was well suited for observations and multi model ensemble of all CCS. Using the RCCS under RCP (Representative Concentration Pathway) 8.5, the future extreme precipitation was projected. As a result, the magnitude and frequency of extreme precipitation increased towards the farther future. Especially, extreme precipitation (daily maximum precipitation of 20-year return-period) during 2070-2099, was projected to occur once every 8.3-year. The RCCS employed in this study is able to successfully represent the performance of all CCS, therefore, this approach can give opportunities managing water resources efficiently for assessment of climate change impacts.

ACS Style

Jang Hyun Sung; Minsung Kwon; Jong-June Jeon; Seung Beom Seo. A Projection of Extreme Precipitation Based on a Selection of CMIP5 GCMs over North Korea. Sustainability 2019, 11, 1976 .

AMA Style

Jang Hyun Sung, Minsung Kwon, Jong-June Jeon, Seung Beom Seo. A Projection of Extreme Precipitation Based on a Selection of CMIP5 GCMs over North Korea. Sustainability. 2019; 11 (7):1976.

Chicago/Turabian Style

Jang Hyun Sung; Minsung Kwon; Jong-June Jeon; Seung Beom Seo. 2019. "A Projection of Extreme Precipitation Based on a Selection of CMIP5 GCMs over North Korea." Sustainability 11, no. 7: 1976.

Preprint
Published: 21 December 2018
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Compositional data have two unique characteristics compared to typical multivariate data: the observed values are nonnegative and their summand is exactly one. To reflect these characteristics, a specific regularized regression model with linear constraints is commonly used. However, linear constraints incur additional computational time, which becomes severe in high-dimensional cases. As such, we propose an efficient solution path algorithm for a $l_1$ regularized regression with compositional data. The algorithm is then extended to a classification model with compositional predictors. We also compare its computational speed with that of previously developed algorithms and apply the proposed algorithm to analyze human gut microbiome data.

ACS Style

Jong-June Jeon; Yongdai Kim; Sungho Won; Hosik Choi. Primal path algorithm for compositional data analysis. 2018, 1 .

AMA Style

Jong-June Jeon, Yongdai Kim, Sungho Won, Hosik Choi. Primal path algorithm for compositional data analysis. . 2018; ():1.

Chicago/Turabian Style

Jong-June Jeon; Yongdai Kim; Sungho Won; Hosik Choi. 2018. "Primal path algorithm for compositional data analysis." , no. : 1.

Journal article
Published: 01 October 2017 in Computational Statistics & Data Analysis
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ACS Style

Jong-June Jeon; Sunghoon Kwon; Hosik Choi. Homogeneity detection for the high-dimensional generalized linear model. Computational Statistics & Data Analysis 2017, 114, 61 -74.

AMA Style

Jong-June Jeon, Sunghoon Kwon, Hosik Choi. Homogeneity detection for the high-dimensional generalized linear model. Computational Statistics & Data Analysis. 2017; 114 ():61-74.

Chicago/Turabian Style

Jong-June Jeon; Sunghoon Kwon; Hosik Choi. 2017. "Homogeneity detection for the high-dimensional generalized linear model." Computational Statistics & Data Analysis 114, no. : 61-74.

Original paper
Published: 25 August 2017 in Theoretical and Applied Climatology
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Regional frequency analysis (RFA) based on L-moments is widely used in the analysis of hydrological process. However, L-moments are defined by order statistics from a stationary distribution and thus theoretically good properties of RFA do not hold under a nonstationary distribution. In this study, a procedure was proposed for an RFA based on L-moments for nonstationary hydrological processes. The proposed method uses a distribution-free de-trended method to apply the conventional RFA based on L-moments. The conventional and the proposed RFAs for annual maximum precipitation in South Korea were compared, and the simulation results indicated that the proposed RFA could provide proper information regarding heterogeneity in a nonstationary distribution. The estimated results of return levels, the median in the nonstationary RFA, were higher than in the conventional RFA.

ACS Style

Jang Hyun Sung; Young-Oh Kim; Jong-June Jeon. Application of distribution-free nonstationary regional frequency analysis based on L-moments. Theoretical and Applied Climatology 2017, 133, 1219 -1233.

AMA Style

Jang Hyun Sung, Young-Oh Kim, Jong-June Jeon. Application of distribution-free nonstationary regional frequency analysis based on L-moments. Theoretical and Applied Climatology. 2017; 133 (3-4):1219-1233.

Chicago/Turabian Style

Jang Hyun Sung; Young-Oh Kim; Jong-June Jeon. 2017. "Application of distribution-free nonstationary regional frequency analysis based on L-moments." Theoretical and Applied Climatology 133, no. 3-4: 1219-1233.

Journal article
Published: 01 July 2016 in Journal of Hydrology
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ACS Style

Jong-June Jeon; Jang Hyun Sung; Eun-Sung Chung. Abrupt change point detection of annual maximum precipitation using fused lasso. Journal of Hydrology 2016, 538, 831 -841.

AMA Style

Jong-June Jeon, Jang Hyun Sung, Eun-Sung Chung. Abrupt change point detection of annual maximum precipitation using fused lasso. Journal of Hydrology. 2016; 538 ():831-841.

Chicago/Turabian Style

Jong-June Jeon; Jang Hyun Sung; Eun-Sung Chung. 2016. "Abrupt change point detection of annual maximum precipitation using fused lasso." Journal of Hydrology 538, no. : 831-841.

Journal article
Published: 01 July 2016 in Journal of Hydrology
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In this study, we develop a new method for a Bayesian change point analysis. The proposed method is easy to implement and can be extended to a wide class of distributions. Using a the generalized extreme-value distribution, we investigate the annual maximum of precipitations observed at stations in the South Korean Peninsula, and find significant changes in the considered sites. We evaluate the hydrological risk in predictions using the estimated return levels. In addition, we explain that the misspecification of the probability model can lead to a bias in the number of change points and using a simple example, show that this problem is difficult to avoid by technical data transformation.

ACS Style

Seongil Jo; Gwangsu Kim; Jong-June Jeon. Bayesian analysis to detect abrupt changes in extreme hydrological processes. Journal of Hydrology 2016, 538, 63 -70.

AMA Style

Seongil Jo, Gwangsu Kim, Jong-June Jeon. Bayesian analysis to detect abrupt changes in extreme hydrological processes. Journal of Hydrology. 2016; 538 ():63-70.

Chicago/Turabian Style

Seongil Jo; Gwangsu Kim; Jong-June Jeon. 2016. "Bayesian analysis to detect abrupt changes in extreme hydrological processes." Journal of Hydrology 538, no. : 63-70.

Journal article
Published: 10 November 2011 in Medical Physics
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To propose multiple logistic regression (MLR) and artificial neural network (ANN) models constructed using digital imaging and communications in medicine (DICOM) header information in predicting the fidelity of Joint Photographic Experts Group (JPEG) 2000 compressed abdomen computed tomography (CT) images.

ACS Style

Kil Joong Kim; Bohyoung Kim; Hyunna Lee; Hosik Choi; Jong-June Jeon; Jeong-Hwan Ahn; Kyoung Ho Lee. Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information. Medical Physics 2011, 38, 6449 -6457.

AMA Style

Kil Joong Kim, Bohyoung Kim, Hyunna Lee, Hosik Choi, Jong-June Jeon, Jeong-Hwan Ahn, Kyoung Ho Lee. Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information. Medical Physics. 2011; 38 (12):6449-6457.

Chicago/Turabian Style

Kil Joong Kim; Bohyoung Kim; Hyunna Lee; Hosik Choi; Jong-June Jeon; Jeong-Hwan Ahn; Kyoung Ho Lee. 2011. "Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information." Medical Physics 38, no. 12: 6449-6457.

Journal article
Published: 31 August 2011 in Advances in Water Resources
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Two well-known methods for estimating statistical distributions in hydrology are the Method of Moments (MOMs) and the method of probability weighted moments (PWM). This paper is concerned with the case where a part of the sample is censored. One situation where this might occur is when systematic data (e.g. from gauges) are combined with historical data, since the latter are often only reported if they exceed a high threshold. For this problem, three previously derived estimators are the “B17B” estimator, which is a direct modification of MOM to allow for partial censoring; the “partial PWM estimator”, which similarly modifies PWM; and the “expected moments algorithm” estimator, which improves on B17B by replacing a sample adjustment of the censored-data moments with a population adjustment. The present paper proposes a similar modification to the PWM estimator, resulting in the “expected probability weighted moments (EPWM)” estimator. Simulation comparisons of these four estimators and also the maximum likelihood estimator show that the EPWM method is at least competitive with the other four and in many cases the best of the five estimators.

ACS Style

Jong-June Jeon; Young-Oh Kim; Yongdai Kim. Expected probability weighted moment estimator for censored flood data. Advances in Water Resources 2011, 34, 933 -945.

AMA Style

Jong-June Jeon, Young-Oh Kim, Yongdai Kim. Expected probability weighted moment estimator for censored flood data. Advances in Water Resources. 2011; 34 (8):933-945.

Chicago/Turabian Style

Jong-June Jeon; Young-Oh Kim; Yongdai Kim. 2011. "Expected probability weighted moment estimator for censored flood data." Advances in Water Resources 34, no. 8: 933-945.