Article
Computer Science, Interdisciplinary Applications
Kailun Zhu, Dorota Kurowicka, Gabriela F. Nane
Summary: A new R-vine forward regression method is proposed, using a heuristic approach to determine the most appropriate structure for modeling the conditional distribution of the response variable. The method can be extended to situations with multiple response variables and is applied to stress analysis in the manufacturing sector, demonstrating its impact on the overall economy.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2021)
Article
Nuclear Science & Technology
Md Tanjin Amin, Yuantao Yao, Jie Yu, Sidum Adumene
Summary: Safety assessment is crucial for nuclear plants, and the fault detection and diagnosis (FDD) module plays a vital role in ensuring plant safety. This study proposes a multivariate probabilistic framework using an R-vine copula for FDD in nuclear plants. The framework can provide diagnosis reports for different fault types without requiring detailed fault information and can capture data nonlinearity and non-Gaussianity. It has been tested and compared with other multivariate statistical tools, showing better monitoring capabilities. This research provides new insights into multivariate probabilistic safety assessment using vine copula models.
ANNALS OF NUCLEAR ENERGY
(2023)
Article
Engineering, Industrial
Fan Wang, Heng Li, Chao Dong
Summary: This paper presents a D-vine copula marginal regression model for count time series data, which is applied to near-miss count data collected from a construction project over 5 years. The study shows that hidden dependence has a relatively large time-lag and is strongly non-Gaussian, resulting in better predictive performance compared to conventional methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Letter
Engineering, Industrial
Fan Wang, Heng Li, Chao Dong
Summary: This article responds to a commentary published in Reliability Engineering and System Safety, addressing the theoretical assumptions, effectiveness, and practical application of the proposed approach to promote construction site safety.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Mathematical & Computational Biology
Shaojun Zhu, Jinhui Zhao, Yating Wu, Qingshan She
Summary: This paper proposes a new and effective method for entropy transfer by applying R-vine copula function estimation. The experiment results show that the proposed method can accurately infer complex causal coupling and provide a new theoretical perspective for the diagnosis of neuromuscular fatigue and sports rehabilitation.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Engineering, Environmental
Md Tanjin Amin, Faisal Khan, Salim Ahmed, Syed Imtiaz
Summary: This paper proposes a risk-based fault detection and diagnosis methodology using R-vine copula and event tree for nonlinear and nonGaussian process systems. The methodology shows better performance in detecting and diagnosing abnormal situations compared to conventional techniques.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Article
Engineering, Civil
Xu Wang, Yong-Ming Shen
Summary: This study proposes an accurate and reliable stochastic streamflow generation approach based on the regular vine copula model, considering temporal and spatial dependence. The approach divides the vine copula model construction into two independent parts using an R-statistic based strategy, avoiding continuous accumulation of uncertainty. Case studies in two diverse hydrology regions show better performance than existing models, preserving the distribution and statistical characteristics of observed records. The proposed R-vine copula model shows low sensitivity to predictor variables and good adaptability and robustness to diverse streamflow series.
JOURNAL OF HYDROLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Shisong Liu, Shaojun Li
Summary: In this paper, a multi-model D vine copula regression model with vine copula-based dependence description (VCDD-MCR) is proposed to address the problem of low prediction accuracy in soft sensor modeling. The effectiveness of the proposed method is demonstrated using numerical and industrial examples.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Environmental Sciences
George Pouliasis, Gina Alexandra Torres-Alves, Oswaldo Morales-Napoles
Summary: Synthetic time series generation is crucial in contemporary water sciences due to their wide applicability and ability to model environmental uncertainty. Vine copula models offer an appealing approach for this purpose by preserving marginal distributions while modeling various probabilistic dependence structures. The study focuses on stochastic modeling of hydroclimatic processes using vine copula models, presenting an innovative approach to modeling intermittency and multiple processes simultaneously through the coupling of temporal and spatial dependencies.
Article
Physics, Multidisciplinary
Xiaoming Zhang, Tong Zhang, Chien-Chiang Lee
Summary: This study focuses on the direct and indirect spillovers of systemic risk among East Asian, European, and U.S. stock markets under the COVID-19 pandemic. The results show that Hong Kong experienced the largest change in risk after the pandemic erupted, and the risks from European and U.S. stock markets are transmitted to China domestically through Hong Kong and Japan.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Yuan Zhao, QingYao Liu, Junwei Kuang, Kaigui Xie, Weiming Du
Summary: This paper introduces a method to construct an accurate dependence model in the power system, using a nonparametric pair-copula construction (NPCC) to model multivariate correlations accurately. Marginal PDFs and bivariate copula densities are estimated in a data-driven mode, and a PDF transformation method is proposed to avoid variable transformation issues.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Economics
Kajal Lahiri, Liu Yang
Summary: We developed a new econometric model that combines multiple predictors to predict binary outcomes. The model uses the pair-copula construction (PCC) and allows the conditional copula to depend on the conditioning variable in a nonparametric way. We applied this methodology to predict US business cycle peaks 6 months in advance using three leading indicators, and found that it outperformed popular combination models in terms of predictive accuracy measured by the receiver operating characteristic curve. We also evaluated the probability forecasts generated by these models using various diagnostic tools to assess their skill.
EMPIRICAL ECONOMICS
(2023)
Article
Economics
Hyuna Jang, Jong-Min Kim, Hohsuk Noh
Summary: In this article, a vine copula Granger causality test is proposed based on the semi-parametric time-series modeling technique. This test overcomes the limitations of traditional methods and has a computational advantage.
ECONOMIC MODELLING
(2022)
Article
Engineering, Multidisciplinary
Mengqiu Fang, Yue Xiang, Bohan Xu, Tianhao Wang, Li Pan, Youbo Liu, Junyong Liu
Summary: This article proposes a complete framework for load pattern identification, which consists of clustering and classification modules, to mine and analyze massive residential power consumption information. By introducing multi-dimensional scaling and an innovative mixture model, better clustering and classification performance are achieved.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2022)
Article
Meteorology & Atmospheric Sciences
David Jobst, Annette Moeller, Jurgen Gross
Summary: Current weather prediction relies on numerical weather prediction (NWP) models, which still have bias and dispersion errors and lack calibration. Statistical postprocessing using a D-vine copula approach is proposed to improve ensemble forecasts. The performance of D-vine-based method is comparable to tEMOS when only wind speed variables are used and is substantially better with the integration of additional variables. The global DVQR outperforms tEMOS-GB in general, while the local DVQR performs better at specific stations with nonlinear relationships among variables.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2023)
Article
Health Care Sciences & Services
Hyokyoung G. Hong, Hong-Su An, Erin Sarzynski, Kathleen Oberst
Summary: The study compared two composite ADL measures created by exploratory factor analysis and additive modeling and found that the self-care-based ADL limitations composite measure performed equally well in predicting nursing home admission as an additive measure considering all ADL limitations. This approach demonstrated improved interpretability while requiring just five measures.
MEDICAL CARE RESEARCH AND REVIEW
(2021)
Article
Physics, Multidisciplinary
Alex Pijyan, Qi Zheng, Hyokyoung G. Hong, Yi Li
Article
Statistics & Probability
Zhe Fei, Qi Zheng, Hyokyoung G. Hong, Yi Li
Summary: This study proposes a novel method within the framework of global censored quantile regression to draw inference on the effects of high-dimensional predictors. The method investigates covariate-response associations over an interval of quantile levels and properly quantifies the uncertainty of the estimates.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2023)
Article
Multidisciplinary Sciences
Jiyon Lee, Rebecca E. Cash, Remle P. Crowe, Hyokyoung G. Hong, Ashish R. Panchal, Kami Silk, Marvin Helmker, Laura Bix
Summary: This study investigated packaging difficulties, coping strategies, and potential impacts on patient care in prehospital settings. Results showed that nearly 20% of respondents experienced difficulties identifying or opening medications and medical supplies in the past year, with a small percentage reporting negative impacts on patient care.
Letter
Medicine, General & Internal
Chunyang Li, Hyokyoung G. Hong, Zhiye Ying, Xiaoxi Zeng, Yi Li
CHINESE MEDICAL JOURNAL
(2022)
Article
Statistics & Probability
Alexander Kreuzer, Luciana Dalla Valle, Claudia Czado
Summary: Air pollution is a serious issue that can cause harm to human health. This paper proposes a new method to estimate the concentration of fine particulate matter and meteorological data in Beijing in 2014, which is flexible and accurately captures unusual high levels of air pollution.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
(2022)
Article
Medicine, General & Internal
Eleanor L. Watts, Charles E. Matthews, Joshua R. Freeman, Jessica S. Gorzelitz, Hyokyoung G. Hong, Linda M. Liao, Kathleen M. McClain, Pedro F. Saint-Maurice, Eric J. Shiroma, Steven C. Moore
Summary: Higher amounts of physical activity are associated with increased longevity. Different types of leisure time physical activities have different associations with mortality risk. Participating in 7.5 to less than 15 MET hours per week of any activity is significantly associated with reduced mortality risk.
Article
Nursing
Ann Annis, Hyokyoung G. Hong
Summary: This study conducted an observational study using Medicare Public Use Files from 2015 to 2018. It found that although chronic care management services increased each year, they remained underutilized. Increases in beneficiaries, percentage of dually enrolled, and primary care services predicted higher utilization of chronic care management.
Article
Nutrition & Dietetics
Ting Zhang, Sabine Naudin, Hyokyoung G. Hong, Demetrius Albanes, Satu Mannisto, Stephanie J. Weinstein, Steven C. Moore, Rachael Z. Stolzenberg-Solomon
Summary: This study examined the associations between dietary quality indices and serum lipidomic profiles. The results showed that adherence to the Healthy Eating Index (HEI)-2015, Alternate HEI-2010 (AHEI-2010), and alternate Mediterranean Diet Index (aMED) were associated with serum lipid species, particularly triacylglycerols and docosahexaenoic acid (DHA)-containing species, which were related to components of seafood and plant proteins, eicosapentaenoic acid-DHA, fish, or fat ratio.
JOURNAL OF NUTRITION
(2023)
Article
Nutrition & Dietetics
Lauren E. O'Connor, Kevin D. Hall, Kirsten A. Herrick, Jill Reedy, Stephanie T. Chung, Michael Stagliano, Amber B. Courville, Rashmi Sinha, Neal D. Freedman, Hyokyoung G. Hong, Paul S. Albert, Erikka Loftfield
Summary: This study aimed to identify metabolites that differ between dietary patterns high in or void of ultraprocessed foods (UPF) and provide insights into how UPF influences health. The results showed that there were 257 plasma metabolites and 606 24-hour urine metabolites that differed between the UPF dietary pattern and the unprocessed dietary pattern. These differential metabolites could serve as candidate biomarkers for UPF intake or metabolic response.
JOURNAL OF NUTRITION
(2023)
Article
Nutrition & Dietetics
Jungeun Lim, Hyokyoung G. G. Hong, Stephanie J. Weinstein, Mary C. Playdon, Amanda J. Cross, Rachael Stolzenberg-Solomon, Neal D. Freedman, Jiaqi Huang, Demetrius Albanes
Summary: The effects of vitamin E supplementation on cancer and other chronic diseases are unclear. This study compared the serum metabolomic profile of different vitamin E dosages and found significant associations between vitamin E supplementation and various metabolites, including C-22 lactone sulfate and androgens. The study also discovered distinct responses in steroid hormone pathways based on vitamin E dosages. Further research is needed to better understand the biological effects of vitamin E in relation to cancer and other chronic diseases.
Article
Mathematics, Interdisciplinary Applications
Eun Ryung Lee, Seyoung Park, Sang Kyu Lee, Hyokyoung G. Hong
Summary: Existing prediction models are not tailored to individual interests and mainly target average people. We propose a quantile forward regression model for high-dimensional survival data to accommodate the heterogeneous characteristics of covariates and provide a flexible risk model.
LIFETIME DATA ANALYSIS
(2023)
Article
Mathematical & Computational Biology
Seyoung Park, Eun Ryung Lee, Hyokyoung G. G. Hong
Summary: In this paper, a novel framework for dynamic modeling of the associations between health outcomes and risk factors is proposed, which captures the time-varying effects of age. The proposed method combines varying-coefficients regional quantile regression via K-nearest neighbors fused Lasso to better model the effects of risk factors on health outcomes.
STATISTICS IN MEDICINE
(2023)
Article
Health Policy & Services
Sabrina Ford, Kathleen Oberst, Joan Ilardo, Hong Su An, Nicole Jones, Hyokyoung G. Hong, Karen Clark, Zhehui Luo
Summary: This project examined the preferred mode of response to a health services survey, finding that the majority of participants preferred to respond via the internet, with a significant proportion using smartphones. The study also identified differences in internet usage based on race and income, highlighting the persistence of the digital divide. These findings can inform future health programming and telehealth initiatives, particularly in light of the COVID-19 pandemic.
JOURNAL OF HEALTH CARE FOR THE POOR AND UNDERSERVED
(2022)
Article
Economics
Ozge Sahin, Claudia Czado
Summary: A novel vine copula mixture model is proposed to capture asymmetric tail dependencies and non-elliptical clusters in continuous data. The model selection and parameter estimation problems are discussed, and a new model-based clustering algorithm is formulated.
ECONOMETRICS AND STATISTICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Blair Robertson, Chris Price
Summary: Spatial sampling designs are crucial for accurate estimation of population parameters. This study proposes a new design method that generates samples with good spatial spread and performs favorably compared to existing designs.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Hiroya Yamazoe, Kanta Naito
Summary: This paper focuses on the simultaneous confidence region of a one-dimensional curve embedded in multi-dimensional space. An estimator of the curve is obtained through local linear regression on each variable in multi-dimensional data. A method to construct a simultaneous confidence region based on this estimator is proposed, and theoretical results for the estimator and the region are developed. The effectiveness of the region is demonstrated through simulation studies and applications to artificial and real datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Cheng Peng, Drew P. Kouri, Stan Uryasev
Summary: This paper introduces a novel optimal experimental design method for quantifying the distribution tails of uncertain system responses. The method minimizes the variance or conditional value-at-risk of the upper bound of the predicted quantile, and estimates the data uncertainty using quantile regression. The optimal design problems are solved as linear programming problems, making the proposed methods efficient even for large datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiaofei Wu, Hao Ming, Zhimin Zhang, Zhenyu Cui
Summary: This paper proposes a model that combines quantile regression and fused LASSO penalty, and introduces an iterative algorithm based on ADMM to solve high-dimensional datasets. The paper proves the global convergence and comparable convergence rates of the algorithm, and analyzes the theoretical properties of the model. Numerical experimental results support the superior performance of the model.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xin He, Xiaojun Mao, Zhonglei Wang
Summary: This paper proposes a nonparametric imputation method with sparsity to estimate the finite population mean, using an efficient kernel method and sparse learning for estimation. An augmented inverse probability weighting framework is adopted to achieve a central limit theorem for the proposed estimator under regularity conditions.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Christian H. Weiss, Fukang Zhu
Summary: This study introduces a multiplicative error model (CMEMs) for discrete-valued count time series, which is closely related to the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models. It derives the stochastic properties and estimation approaches of different types of INGARCH-CMEMs, and demonstrates their performance and application through simulations and real-world data examples.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Ming-Hung Kao, Ping-Han Huang
Summary: Optimal designs for sparse functional data under the functional empirical component (FEC) settings are investigated. New computational methods and theoretical results are developed to efficiently obtain optimal exact and approximate designs. A hybrid exact-approximate design approach is proposed and demonstrated to be efficient through simulation studies and a real example.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Mateus Maia, Keefe Murphy, Andrew C. Parnell
Summary: The Bayesian additive regression trees (BART) model is a powerful ensemble method for regression tasks, but its lack of smoothness and explicit covariance structure can limit its performance. The Gaussian processes Bayesian additive regression trees (GP-BART) model addresses this limitation by incorporating Gaussian process priors, resulting in superior performance in various scenarios.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xichen Mou, Dewei Wang
Summary: Human biomonitoring is a method of monitoring human health by measuring the accumulation of harmful chemicals in the body. To reduce the high cost of chemical analysis, researchers have adopted a cost-effective approach that combines specimens and analyzes the concentration of toxic substances in the pooled samples. To effectively interpret these aggregated measurements, a new regression framework is proposed by extending the additive partially linear model (APLM). The APLM is versatile in capturing the complex association between outcomes and covariates, making it valuable in assessing the complex interplay between chemical bioaccumulation and potential risk factors.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Lili Yu, Yichuan Zhao
Summary: The classical accelerated failure time model is a linear model commonly used for right censored survival data, but it cannot handle heteroscedastic survival data. This paper proposes a Laplace approximated quasi-likelihood method with a continuous estimating equation to address this issue, and provides estimation bias and confidence interval estimation formulas.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaobo Jin, Youngjo Lee
Summary: Hierarchical generalized linear models are widely used for fitting random effects models, but the standard error estimators receive less attention. Current standard error estimation methods are not necessarily accurate, and a sandwich estimator is proposed to improve the accuracy of standard error estimation.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Rebeca Pelaez, Ingrid Van Keilegom, Ricardo Cao, Juan M. Vilar
Summary: This article proposes an estimator for the probability of default (PD) in credit risk, derived from a nonparametric conditional survival function estimator based on cure models. The asymptotic expressions for bias, variance, and normality of the estimator are presented. Through simulation and empirical studies, the performance and practical behavior of the nonparametric estimator are compared with other methods.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
L. M. Andre, J. L. Wadsworth, A. O'Hagan
Summary: This paper proposes a dependence model that captures the entire data range in multi-variable cases. By blending two copulas with different characteristics and using a dynamic weighting function for smooth transition, the model is able to flexibly capture various dependence structures.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Niwen Zhou, Xu Guo, Lixing Zhu
Summary: The paper investigates hypothesis testing regarding the potential additional contributions of other covariates to the structural function, given the known covariates. The proposed distance-based test, based on Neyman's orthogonality condition, effectively detects local alternatives and is robust to the influence of nuisance functions. Numerical studies and real data analysis demonstrate the importance of this test in exploring covariates associated with AIDS treatment effects.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Blake Moya, Stephen G. Walker
Summary: A full posterior analysis method for nonparametric mixture models using Gibbs-type prior distributions, including the well known Dirichlet process mixture (DPM) model, is presented. The method removes the random mixing distribution and enables a simple-to-implement Markov chain Monte Carlo (MCMC) algorithm. The removal procedure reduces some of the posterior uncertainty and introduces a novel replacement approach. The method only requires the probabilities of a new or an old value associated with the corresponding Gibbs-type exchangeable sequence, without the need for explicit representations of the prior or posterior distributions. This allows the implementation of mixture models with full posterior uncertainty, including one introduced by Gnedin. The paper also provides numerous illustrations and introduces an R-package called CopRe that implements the methodology.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
