Article
Multidisciplinary Sciences
Shu-Fei Wu
Summary: This paper presents interval estimation methods for the scale parameter of two-parameter exponential distribution, and proposes two methods for the joint confidence region. The optimal method of the confidence region is determined by simulation comparison based on confidence region area. The proposed methods are validated through a biometrical example.
Article
Remote Sensing
Jin Xu, Lindi J. Quackenbush, Timothy A. Volk, Stephen Stehman
Summary: This study evaluated the uncertainty of shrub willow health characterization based on unmanned aerial systems (UAS) data. The results showed that regression models built at different spatial scales could be applied across time, space, and scales. The study also quantified the uncertainty of model parameters and found that the uncertainty increased as pixel size increased. The findings provide guidance for future experimental design to save resources.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Pharmacology & Pharmacy
Bryan S. Nelson, Lingyun Liu, Cyrus Mehta
Summary: Statistical methods for controlling type-I error in adaptive group sequential clinical trials are well established, but methods for obtaining statistically valid point estimates and confidence intervals are not as well understood. At the end of an adaptive trial, obtaining statistically valid point estimates and confidence intervals can be achieved through methods like the BWCI and RCI, with the BWCI providing exact coverage and the RCI providing conservative coverage.
PHARMACEUTICAL STATISTICS
(2022)
Article
Mathematics
Tianyu Liu, Lulu Zhang, Guang Jin, Zhengqiang Pan
Summary: An improved E-Bayesian estimation method is proposed in this paper to evaluate product reliability under heavily censored data. The proposed method can achieve both point and confidence interval estimation for the reliability parameters by limiting the value of product failure probability within a certain range through analyzing the characteristics of the Weibull distribution. An improved weighted least squares method is utilized to construct the confidence interval estimation model of reliability parameters. Simulation results show that the proposed approach can significantly improve the calculation speed and estimation accuracy with minimal reductions in robustness. Finally, a real-world case study of the sun gear transmission mechanism is used to validate the effectiveness of the presented method.
Article
Engineering, Marine
Wang Rupeng, Li Ye, Ma Teng, Cong Zheng, Gong Yusen, Jiang Yanqing, Zhang Qiang
Summary: This study proposes a new terrain-aided position model and tide level estimation method to address the issue of robust terrain-aided positioning under unknown tidal and measurement errors. The effectiveness of the algorithms and confidence interval estimation are verified using shipborne measurement data, with future focus on the relation between terrain characteristics and directionality of the confidence interval in terrain-aided positioning.
JOURNAL OF MARINE SCIENCE AND TECHNOLOGY
(2022)
Article
Physics, Multidisciplinary
Wenli Deng, Jinglong Wang
Summary: This paper presents a summation formula of the derivative of the logarithmic Riemann function based on the Euler product formula and provides a more convenient method for calculating maximum likelihood estimation. By compressed transformation, the variance is ensured to be finite, and a method of interval estimation based on the central limit theorem is proposed for the exponent parameter. Additionally, an interval estimation method based on the acceptance domain of likelihood ratio test is also provided.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Wisunee Puggard, Sa-Aat Niwitpong, Suparat Niwitpong
Summary: This study introduces four methods for constructing confidence intervals for the coefficient of variation (CV) and the difference between CVs of Birnbaum-Saunders (BS) distributions. A Monte Carlo simulation shows that the highest posterior density (HPD) interval performs best overall. The proposed methods were validated using PM 2.5 concentration data for Chiang Mai, Thailand in March and April 2019, with results consistent with the simulation findings.
Article
Mathematics, Applied
Z. Liu, Y. Yang
Summary: Pharmacokinetics studies the concentration-time profile of drugs in the body, most drugs are eliminated at first order kinetics with nonconstant elimination rate due to metabolic variations and individual differences. This paper introduces an uncertain pharmacokinetic model for mono-compartmental drugs, providing uncertainty distributions, expected values, and confidence intervals for parameters, as well as moment estimations. A numerical example and real data analysis are used to illustrate the proposed methods.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Engineering, Multidisciplinary
Xiaofei Wang, Bing Xing Wang, Xin Pan, Yunkai Hu, Yingpei Chen, Junxing Zhou
Summary: This paper discusses interval estimation for the inverse Gaussian distribution, deriving generalized confidence intervals and prediction intervals. Through simulation, it is shown that the proposed intervals have higher coverage probabilities compared to traditional methods like Wald CIs and bootstrap-p CIs. Additionally, the proposed procedures are illustrated using two examples.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2021)
Article
Engineering, Chemical
Ahmed Ibrahim Shawky, Khushnoor Khan
Summary: The present study focuses on the multi-component stress-strength model based on inverse Weibull distribution. Reliability is obtained using the maximum likelihood method, and the performance of different sample sizes and parameter combinations on reliability is compared using Monte Carlo simulation. A real data set is used to demonstrate the application of the proposed technique in studying the strength and stress of a multicomponent model.
Article
Computer Science, Software Engineering
Vali Tawosi, Federica Sarro, Alessio Petrozziello, Mark Harman
Summary: The study replicates and extends a previous research on multi-objective software effort estimation, strengthening both internal and external validity. The results confirm the superior performance of CoGEE and its effectiveness across different multi-objective algorithms.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Environmental Sciences
Mamoru Tanaka, Tomoya Kataoka, Yasuo Nihei
Summary: Microplastics, plastic particles smaller than 5 mm in diameter, are becoming widespread pollutants in natural environments, including freshwater ecosystems. However, there is currently no standard method for in situ sampling in freshwater environments. This study proposes a framework based on the Poisson point process to compute confidence intervals for microplastic concentration estimates and validates the method using samples from two urban rivers in Chiba, Japan.
ENVIRONMENTAL POLLUTION
(2023)
Article
Engineering, Multidisciplinary
Mykhaylo Dorozhovets
Summary: This article examines the joint and marginal distributions for the location and scale parameters of the double exponential (Laplace) population for different numbers of observations. By transforming the joint distribution of estimators, the distributions are obtained, with the presence of module functions in the population distribution model being a key issue. General procedures for deriving the joint distribution of estimators for odd and even numbers of observations are introduced, along with calculations for expected values, variances, uncertainties, and confidence intervals for population parameters at various confidence levels. The results obtained were validated through Monte-Carlo simulations.
Article
Psychology, Mathematical
Junyeong Yang, Jiwon Kim, Minjung Kim
Summary: This study provides an overview of various methods for detecting dyadic patterns in the actor-partner interdependence model (APIM). By evaluating and comparing four different methods, it was found that the new-variable approach and chi(2) difference test performed better in detecting dyadic patterns. The findings suggest a novel procedure for examining dyadic patterns in APIM.
BEHAVIOR RESEARCH METHODS
(2023)
Article
Statistics & Probability
Liugen Xue
Summary: This paper studies the estimation and empirical likelihood (EL) of the parameters in a partially linear single-index varying-coefficient model. A two-stage method is proposed to estimate the regression parameters and the coefficient functions. The asymptotic distributions of the proposed estimators are obtained. A bias-corrected EL ratio for the regression parameters is also proposed. It is shown that the ratio asymptotically follows a standard chi-squared distribution. The proposed method is evaluated through simulation studies and an application example using a real data set.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2023)
Article
Mathematical & Computational Biology
Nina Zhou, Robert D. Brook, Ivo D. Dinov, Lu Wang
Summary: The wide-scale adoption of electronic health records provides extensive information for precision medicine and personalized healthcare. By leveraging free-text clinical information extraction techniques, optimal dynamic treatment regimes can be estimated, allowing for individualized treatments based on patient characteristics and treatment history.
BIOMETRICAL JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Rongqian Zhang, Yupeng Zhang, Yuyao Liu, Yunjie Guo, Yueyang Shen, Daxuan Deng, Yongkai Joshua Qiu, Ivo D. Dinov
Summary: This paper introduces a new method for representing, modeling, and analyzing repeated-measurement longitudinal data using tensor-based linear modeling and complex time transformations, providing unique analysis opportunities and techniques.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Nina Zhou, Lu Wang, Simeone Marino, Yi Zhao, Ivo D. Dinov
Summary: This study presents a partially synthetic data generation technique for creating anonymized data archives that closely resemble the original sensitive data. This technique reduces the risk of re-identification while preserving the analytical value of the obfuscated data. It provides an automated tool for effective and collaborative analytics for large time-varying datasets containing sensitive information.
JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Wenbo Sun, Dipesh Niraula, Issam El Naqa, Randall K. Ten Haken, Ivo Dinov, Kyle Cuneo, Judy (Jionghua) Jin
Summary: This paper presents a systematic method to integrate expert human knowledge with AI recommendations for optimizing clinical decision making. It combines Gaussian process models with deep neural networks to quantify the uncertainty of treatment outcomes given by physicians and AI recommendations, providing guidance for clinical physicians and improving AI models performance.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Public, Environmental & Occupational Health
Apichai Wattanapisit, Hanif Abdul Rahman, Josip Car, Khadizah Haji Abdul-Mumin, Ma. Henrietta Teresa O. de la Cruz, Michael Chia, Michael Rosenberg, Moon-ho Ringo Ho, Surasak Chaiyasong, Trias Mahmudiono, Yuvadee Rodjarkpai, Ivo D. Dinov, Mohammad Ottom, Areekul Amornsriwatanakul
Summary: This study examines the associations between behavioral characteristics, mental wellbeing, demographic characteristics, and health among university students in the ASEAN University Network - Health Promotion Network. Through cluster analysis, the study identifies five clusters of student-types with distinct health behaviors. The findings suggest that interventions should focus on the dominant health-risk behavior, with consideration given to the associated health-risk behaviors within clusters.
Article
Neurosciences
Seok Woo Moon, Lu Zhao, William Matloff, Sam Hobel, Ryan Berger, Daehong Kwon, Jaebum Kim, Arthur W. W. Toga, Ivo D. D. Dinov
Summary: This study examined the association between genetic and neuroimaging biomarkers in late-onset dementia-related cognitive impairment. The results showed significant correlations between specific genomic markers and neuroimaging markers, and identified key markers for distinguishing Alzheimer's disease and mild cognitive impairment.
CNS NEUROSCIENCE & THERAPEUTICS
(2023)
Article
Oncology
Hanif Abdul Rahman, Mohammad Ashraf Ottom, Ivo D. Dinov
Summary: This study aimed to evaluate machine learning algorithms in large-scale datasets, taking into account both younger and older adults from various regions and sociodemographics. The study found that a prediction model based on an artificial neural network performed well in predicting CRC and non-CRC phenotypes.
Article
Multidisciplinary Sciences
Dipesh Niraula, Wenbo Sun, Jionghua Jin, Ivo D. Dinov, Kyle Cuneo, Jamalina Jamaluddin, Martha M. Matuszak, Yi Luo, Theodore S. Lawrence, Shruti Jolly, Randall K. Ten Haken, Issam El Naqa
Summary: This study developed an artificial intelligence-based decision-making framework to assist in dynamic treatment regimes (DTR) for oncology. The framework utilizes advanced machine learning analytics and information-rich multi-omics data to overcome the challenges posed by various variables, treatment response uncertainty, and patient heterogeneity. The framework, demonstrated in Knowledge Based Response-Adaptive Radiotherapy (KBR-ART) applications, consists of two main components and has shown promising results in improving clinical decision-making and treatment outcomes.
SCIENTIFIC REPORTS
(2023)
Article
Psychiatry
Alexander Weigard, Katherine L. McCurry, Zvi Shapiro, Meghan E. Martz, Mike Angstadt, Mary M. Heitzeg, Ivo D. Dinov, Chandra Sripada
Summary: This study developed and tested machine learning models to predict ADHD symptoms in children using neurocognitive abilities, demographics, and child and family characteristics. The models explained 15-20% of the variance in 1-year ADHD symptoms and 12-13% of the variance in 2-year ADHD symptoms. The models showed high generalizability and minimal predictive power loss when applied to new data.
TRANSLATIONAL PSYCHIATRY
(2023)
Article
Pharmacology & Pharmacy
Simeone Marino, Hassan Jassar, Dajung J. J. Kim, Manyoel Lim, Thiago D. D. Nascimento, Ivo D. D. Dinov, Robert A. A. Koeppe, Alexandre F. F. DaSilva
Summary: This study utilized a novel machine learning method to accurately identify migraine patients based on the analysis of central mu-opioid and dopamine D2/D3 receptors. The results showed that dysfunction in the μ-opioid and D2/D3 receptors in the neurotransmission of migraine patients may partly explain the severe impact of migraine and associated neuropsychiatric comorbidities.
FRONTIERS IN PHARMACOLOGY
(2023)
Article
Biotechnology & Applied Microbiology
Mohammad Ashraf Ottom, Hanif Abdul Rahman, Iyad M. Alazzam, Ivo D. Dinov
Summary: This study proposes an enhanced deep neural network approach, the 3D-Znet model, for segmenting brain tumors based on 3D neuroimaging data. It provides automated tumor diagnostics and can help in early tumor diagnosis, potentially saving lives.
BIOENGINEERING-BASEL
(2023)
Article
Biotechnology & Applied Microbiology
Hanif Abdul Rahman, Madeline Kwicklis, Mohammad Ottom, Areekul Amornsriwatanakul, Khadizah H. Abdul-Mumin, Michael Rosenberg, Ivo D. Dinov
Summary: This study utilized machine learning algorithms and artificial intelligence techniques to assess mental well-being and identified the most significant features associated with it. The findings are of great importance for providing cost-effective support and modernizing mental well-being assessment at both individual and university levels.
BIOENGINEERING-BASEL
(2023)
Meeting Abstract
Oncology
D. Niraula, W. Sun, J. Jin, I. D. Dinov, K. C. Cuneo, J. Jamaluddin, M. M. Matuszak, R. K. Ten Haken, I. El Naqa
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2022)
Article
Health Care Sciences & Services
Nina Zhou, Qiucheng Wu, Zewen Wu, Simeone Marino, Ivo D. Dinov
Summary: This article introduces a new method called DataSifterText, which can generate partially synthetic clinical free-text and provides high utility preservation while protecting privacy. Experiments have shown that this method is superior to traditional content suppression methods in terms of privacy protection and information preservation.
JOURNAL OF MEDICAL SYSTEMS
(2022)
Article
Engineering, Biomedical
Mohammad Ashraf Ottom, Hanif Abdul Rahman, Ivo D. Dinov
Summary: This paper presents a novel framework for segmenting brain tumors in MR images using deep neural networks and data augmentation strategies. The experimental results demonstrate high performance of the proposed method in localizing and segmenting brain tumors in MR images.
IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE
(2022)
