Article
Engineering, Industrial
Susumu Shuto, Takashi Amemiya
Summary: This study investigates the sequential Bayesian inference of the Weibull distribution parameters of system components using failure observations analyzed as censored data. The proposed method, Sequential Bayesian Inference with Optimized Prior Distribution (SBOPD), utilizes prior information and optimized initial prior distribution to estimate parameters more accurately and effectively at the preliminary stages of the system life cycle.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Physics, Multidisciplinary
Rolando de la Cruz, Claudio Fuentes, Oslando Padilla
Summary: Mixture cure rate models are developed to analyze failure time data where a portion of subjects do not experience failure. These models assume that the studied population consists of both susceptible subjects who may experience the event of interest and non-susceptible subjects who never experience it.
Article
Multidisciplinary Sciences
Elisa Ferrari, Luna Gargani, Greta Barbieri, Lorenzo Ghiadoni, Francesco Faita, Davide Bacciu
Summary: We propose a clinical data analysis workflow based on Bayesian Structure Learning (BSL). This workflow incorporates prior medical knowledge into the learning process and provides explainable results in the form of causal connections among analyzed features. Evaluation on a COVID-19 dataset shows that the proposed framework gives a schematic overview of the multi-factorial processes contributing to the outcome and rediscovers established cause-effect relationships. Furthermore, the approach yields a highly interpretable tool that accurately predicts the outcome using a small number of features.
Article
Spectroscopy
Isao Noda
Summary: A procedure is proposed to estimate pure component spectra from a limited number of available spectra, and it can handle systems with more than two component species.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Review
Anesthesiology
Laura Quinn, Tonny Veenith, Julian Bion, Karla Hemming, Tony Whitehouse, Richard Lilford
Summary: This study conducted a Bayesian meta-analysis to examine the effects of early tracheostomy on clinical outcomes in mechanically ventilated patients. The results showed a high posterior probability of benefits in reducing ICU stay and mechanical ventilation duration, but no significant benefits in reducing short-term mortality or ventilator-associated pneumonia.
BRITISH JOURNAL OF ANAESTHESIA
(2022)
Article
Ecology
Kenneth F. Kellner, Nicholas L. Fowler, Tyler R. Petroelje, Todd M. Kautz, Dean E. Beyer, Jerrold L. Belant
Summary: Getting unbiased estimates of wildlife distribution and abundance is an important objective in research and management. Fitting occupancy and N-mixture abundance models in a Bayesian framework using Stan has advantages, but can be challenging for many researchers. The ubms package provides an easy-to-use interface for fitting models and analyzing data, potentially expanding the user base for rigorously assessing species distribution and abundance.
METHODS IN ECOLOGY AND EVOLUTION
(2022)
Article
Computer Science, Artificial Intelligence
Lijuan Yang, Nannan Ji, Changpeng Wang, Tianjun Wu, Fuxiao Li
Summary: A two-phase registration method for three-dimensional point set is proposed under the Bayesian mixture framework. The first phase recovers rotation transformation by performing similarity point set registration using a mixture model of Student's t distribution and von Mises-Fisher distribution. The second phase implements nonrigid registration based on positional information only. Experimental results demonstrate that this method achieves better registration performance in terms of robustness to rotation and outliers.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Ravi Teja Vemuri, Muhammad Azam, Nizar Bouguila, Zachary Patterson
Summary: This paper proposes an effective unsupervised Bayesian framework for learning a finite mixture of asymmetric generalized Gaussian distributions (AGGD). The model is evaluated through experiments and performs better in clustering applications compared to Bayesian Gaussian and Bayesian asymmetric Gaussian mixture models.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Engineering, Industrial
Arthur Henrique de Andrade Melani, Miguel Angelo de Carvalho Michalski, Renan Favarao da Silva, Gilberto Francisco Martha de Souza
Summary: Through Condition-Based Maintenance strategy, a hybrid framework based on Moving Window Principal Component Analysis (MWPCA) and Bayesian Network (BN) was proposed for automated Fault Detection and Diagnosis (FDD) in machinery. The framework was able to detect and diagnose several simulated failures in a simplified model of a hydrogenerator.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Mathematics
Refah Alotaibi, Mervat Khalifa, Ehab M. Almetwally, Indranil Ghosh, H. Rezk
Summary: The study introduces a new bivariate mixture EE model and investigates its statistical properties. Different estimation methods for model parameters are used under the classical and Bayesian paradigm, with simulation studies to verify performance and reanalysis of a real dataset to illustrate flexibility of the proposed model.
JOURNAL OF MATHEMATICS
(2021)
Article
Engineering, Chemical
Gloria M. Monsalve-Bravo, Ravi C. Dutta, Christian C. Zuluaga-Bedoya, Matthew P. Adams, Simon Smart, Muxina Konarova, Suresh K. Bhatia
Summary: Investigation of mixed-gas sorption is crucial for the design and optimization of membrane-based processes. Existing sorption models often deviate from observed mixture data, and parameter uncertainty is often ignored. This study uses Bayesian Inference to estimate probability distributions for sorption models' parameters, providing statistically meaningful mixture sorption forecasts that consider parameter uncertainty. Molecular sorption simulations are conducted to demonstrate the benefits of this technique for uncertainty quantification and propagation in membrane applications.
JOURNAL OF MEMBRANE SCIENCE
(2024)
Article
Engineering, Industrial
El Hassene Ait Mokhtar, Radouane Laggoune, Alaa Chateauneuf
Summary: This paper introduces an original approach for maintenance efficiency modeling of multi-component systems. The proposed approach considers the effect of component replacement on system behavior and provides a hybrid model and an efficient assessment procedure using Bayesian networks.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Automation & Control Systems
Mingming Bai, Yulong Huang, Badong Chen, Yonggang Zhang
Summary: A novel normal-skew mixture (NSM) distribution is introduced in this article to model various noise distributions. Utilizing this distribution, a new robust Kalman filtering framework is developed and several exemplary robust Kalman filters are derived based on it. The proposed framework is proven to have superior performance in target tracking simulation.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Public, Environmental & Occupational Health
Niko A. Kaciroti, Carey Lumeng, Vikas Parekh, Matthew L. Boulton
Summary: The outbreak of SARS-CoV-2 has led to a global pandemic, with the United States experiencing varying mortality curves and peak dates. A Bayesian mixture model proved to be more accurate in predicting these curves and peak timings, essential for informing resource allocation and intervention strategies amidst the dynamic nature of the pandemic.
AMERICAN JOURNAL OF TROPICAL MEDICINE AND HYGIENE
(2021)
Article
Mathematics
Refah Alotaibi, Lamya A. Baharith, Ehab M. Almetwally, Mervat Khalifa, Indranil Ghosh, Hoda Rezk
Summary: This study introduces a new series of distributions, ExpKum-G, which includes a mixture of the exponentiated Kumaraswamy-G distribution and the exponentiated Kumaraswamy Weibull distribution. Through simulation studies and analysis of real datasets, it is demonstrated that this model excels in model fitting.
Letter
Virology
Fabio Divino, Massimo Ciccozzi, Alessio Farcomeni, Giovanna Jona-Lasinio, Gianfranco Lovison, Antonello Maruotti
JOURNAL OF MEDICAL VIROLOGY
(2022)
Article
Statistics & Probability
Alessio Farcomeni, Marco Geraci, Cinzia Viroli
Summary: Classifiers based on directional quantiles are introduced, and theoretical results are derived for selecting optimal quantile levels given a direction and vice versa. It is also shown that the proposed classifier has a probability of correct classification converging to one, under the conditions that population distributions differ by at most a location shift and the number of directions diverges at the same rate as the problem's dimension. The performance of the proposed classifiers is demonstrated through simulation studies and a real data example.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2022)
Letter
Virology
Fabio Divino, Antonello Maruotti, Alessio Farcomeni, Giovanna Jona-Lasinio, Gianfranco Lovison, Massimo Ciccozzi
JOURNAL OF MEDICAL VIROLOGY
(2022)
Article
Social Sciences, Mathematical Methods
Francesco Bartolucci, Alessio Farcomeni
Summary: The study proposes a hidden Markov model with spatial and temporal components to analyze global data on food access from Gallup's world polls. The model is based on discrete latent space and considers area-time-specific covariates and individual-specific covariates affecting food access.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY
(2022)
Article
Mathematics, Interdisciplinary Applications
Roberto Di Mari, Francesco Dotto, Alessio Farcomeni, Antonio Punzo
Summary: A general approach to detect measurement non-invariance in latent Markov models for longitudinal data is proposed, with the use of BIC for model selection. Results from simulation studies and real-data examples in social sciences demonstrate that BIC is able to select the correct measurement equivalence structure more than 95% of the time.
STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL
(2022)
Article
Geosciences, Multidisciplinary
Marco Mingione, Pierfrancesco Alaimo Di Loro, Alessio Farcomeni, Fabio Divino, Gianfranco Lovison, Antonello Maruotti, Giovanna Jona Lasinio
Summary: We introduce an extended generalised logistic growth model for discrete outcomes, which deals with spatial and temporal dependence through a network structure. The study finds substantial spatial and temporal dependence in both waves of COVID-19 in Italy.
SPATIAL STATISTICS
(2022)
Article
Biology
Linda Altieri, Alessio Farcomeni, Danilo Alunni Fegatelli
Summary: We introduce a time-interaction point process that allows for self-excitement and self-correction effects, as well as other factors, in the occurrence of events. The model is applied to capture-recapture data for estimating the total number of drug dealers. We derive a conditional likelihood formulation to estimate the population size and demonstrate the effectiveness of our approach through simulation and analysis of a motivating example.
Review
Anesthesiology
Lior Mevorach, Ali Forookhi, Alessio Farcomeni, Stefano Romagnoli, Federico Bilotta
Summary: This study conducted a systematic review and meta-analysis of 169 studies and identified age, ASA physical status, Charlson Comorbidity Index, and Mini-Mental State Examination as significant risk factors for postoperative delirium.
BRITISH JOURNAL OF ANAESTHESIA
(2023)
Article
Mathematical & Computational Biology
Alessio Farcomeni
Summary: This study proposes a likelihood ratio test for assessing the completeness of sampling in closed population size estimation studies. The test determines whether the expected number of unsampled subjects falls below a threshold specified by the user. The test statistic has a nonstandard distribution under the null hypothesis, and critical values can be easily approximated and tabulated regardless of model specification. The effectiveness of the test is demonstrated through simulation and real data examples, including a case involving ascertainment bias in Gulf War veterans with amyotrophic lateral sclerosis.
BIOMETRICAL JOURNAL
(2023)
Article
Nutrition & Dietetics
Silverio Rotondi, Lida Tartaglione, Marzia Pasquali, Maria Jose Ceravolo, Anna Paola Mitterhofer, Annalisa Noce, Monica Tavilla, Silvia Lai, Francesca Tinti, Maria Luisa Muci, Alessio Farcomeni, Sandro Mazzaferro
Summary: This study evaluated the association between cognitive impairment (evaluated using MoCA scores) and nutritional status (evaluated using MIS scores) in hemodialysis patients. The results showed that both cognitive impairment and malnutrition were prevalent in hemodialysis patients, and malnutrition was identified as a risk factor for cognitive impairment.
Article
Medicine, General & Internal
Silverio Rotondi, Lida Tartaglione, Maria Luisa Muci, Marzia Pasquali, Nicola Panocchia, Filippo Aucella, Antonio Gesuete, Teresa Papalia, Luigi Solmi, Alessio Farcomeni, Sandro Mazzaferro
Summary: Patients on haemodialysis have a high mortality rate due to subclinical hypoxic parenchymal stress during HD sessions. This study investigated whether changes in the oxygen extraction ratio (OER) during HD could predict mortality risk. The results showed that patients with a Delta OER >= 40% had a higher incidence of death, suggesting that Delta OER >= 40% is a significant mortality risk factor in HD patients.
JOURNAL OF CLINICAL MEDICINE
(2023)
Article
Oncology
Ferdinando Corica, Maria Silvia De Feo, Maria Lina Stazza, Maria Rondini, Andrea Marongiu, Viviana Frantellizzi, Susanna Nuvoli, Alessio Farcomeni, Giuseppe De Vincentis, Angela Spanu
Summary: This study evaluates the reliability of qualitative and semiquantitative parameters of F-18-FDG PET-CT in predicting the risk of malignancy in patients with solitary pulmonary nodules (SPNs) before the diagnosis of lung cancer. The results show that these parameters are reliable tools, with good sensitivity and specificity, in predicting malignancy.
Review
Pharmacology & Pharmacy
Gianluca Gazzaniga, Danilo Menichelli, Francesco Scaglione, Alessio Farcomeni, Arianna Pani, Daniele Pastori
Summary: This systematic umbrella review with meta-analysis evaluated the certainty of evidence on the mortality risk associated with digoxin use in patients with atrial fibrillation. The results suggest that the use of digoxin is associated with a moderate increased risk of all-cause and cardiovascular mortality in AF patients, regardless of the presence of heart failure.
EUROPEAN JOURNAL OF CLINICAL PHARMACOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Maria Silvia De Feo, Viviana Frantellizzi, Matteo Bauckneht, Alessio Farcomeni, Luca Filippi, Elisa Lodi Rizzini, Valentina Lavelli, Maria Lina Stazza, Tania Di Raimondo, Giuseppe Fornarini, Sara Elena Rebuzzi, Mammini Filippo, Paolo Mammucci, Andrea Marongiu, Fabio Monari, Giuseppe Rubini, Angela Spanu, Giuseppe De Vincentis
Summary: This multicenter study aimed to assess the impact of baseline bone scan index (BSI) on overall survival (OS) in mCRPC patients treated with (RaCl2)-Ra-223. The results showed that baseline BSI significantly predicts OS in mCRPC treated with (RaCl2)-Ra-223.
Article
Computer Science, Artificial Intelligence
Luca Bagnato, Alessio Farcomeni, Antonio Punzo
Summary: This paper revisits the generalized hyperbolic distribution and its nested models, and introduces a novel penalized method for choosing among alternative constraints on the same parameter. The proposed method simultaneously performs model selection and inference within the GH family. The effectiveness of the method is demonstrated through simulation studies and a real data example.
STATISTICAL ANALYSIS AND DATA MINING
(2023)
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)
