Article
Statistics & Probability
Tsung- Lin, Wan-Lun Wang
Summary: This paper derives explicit expressions for the moments of truncated multivariate normal/independent distributions with supports confined within a hyper-rectangle. A Monte Carlo experiment is conducted to validate the proposed formulae for five selected members of the distributions.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
Article
Mathematics, Interdisciplinary Applications
Yang Sun, Xiangzhong Fang
Summary: Recently, more attention has been given to uncertainty quantification in computer model calibration. However, most existing papers assume errors follow a Gaussian or sub-Gaussian distribution, which is not realistic. To overcome this limitation, the authors propose a robust calibration procedure based on Huber loss that can effectively deal with responses containing outliers and heavy-tail errors. Two different estimators of the calibration parameters are proposed using ordinary least squares and L2 calibration, respectively. Through numerical simulations and a real example, the authors verify the good performance of the proposed calibration procedure.
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
(2023)
Article
Chemistry, Analytical
Maxime Metz, Florent Abdelghafour, Jean-Michel Roger, Matthieu Lesnoff
Summary: The paper presents a novel robust PLSR algorithm, RoBoost-PLSR, inspired by boosting principles, which mitigates the impact of outliers during calibration. Compared to other algorithms, RoBoost-PLSR demonstrates resilience and good performance on multiple datasets.
ANALYTICA CHIMICA ACTA
(2021)
Article
Engineering, Multidisciplinary
Jose Ragot
Summary: Detecting and locating outliers in measurements used for monitoring systems is crucial. Redundant information is needed for this. Sometimes, a robust approach that minimizes the impact of outliers is preferred.
Article
Mathematics, Interdisciplinary Applications
Philippe Gagnon
Summary: Including prior information about model parameters in Bayesian statistical analysis has both positive and negative views. It allows for incorporating expert opinion, but it also introduces subjectivity. Problems arise when there is a conflict with the collected data, and the impact of conflicting prior information can be diminished by using heavy-tailed priors. In this study, the efficacy of this solution is examined in a regression framework using different types of tail decay functions.
Article
Agriculture, Dairy & Animal Science
P. M. Iglesias, I. Camerlink
Summary: This study investigated the relationship between pigs' natural behavior and their tail posture and motion. The results showed that tail posture and motion were associated with different behaviors, but hanging tails were not primarily linked to tail biting behavior.
Article
Engineering, Mechanical
Xiaokai Wei, Jie Li, Debiao Zhang, Kaiqiang Feng
Summary: An improved factor graph method based on enhanced robustness is proposed to improve the navigation performance and robustness of an INS/GPS/OD integrated navigation system. By dynamically adjusting factor weights, the method achieved a significant increase in navigation accuracy and outperformed existing methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Chemistry, Analytical
Yifan Wang, Guolin Yu, Jun Ma
Summary: In this paper, a novel robust loss function is designed and a new binary classification learning method is proposed to improve classification performance and robustness while reducing the influence of outliers on the model. The introduction of regularization terms realizes the principle of structural risk minimization, and a simple and efficient iterative algorithm is designed to solve the non-convex optimization problem.
Article
Multidisciplinary Sciences
Thomas J. Sargent, Neng Wang, Jinqiang Yang
Summary: According to the study, wealth is distributed more unequally than labor earnings, influenced by luck, attitudes towards saving decisions, and growth rates of labor earnings. Strong motives for people to save and firms to demand capital raise the equilibrium interest rate, causing wealth to grow faster than labor earnings and resulting in a more uneven distribution of wealth compared to labor earnings.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Management
Alexander D. Stead, Phill Wheat, William H. Greene
Summary: The robustness of efficiency scores in decision-making units is important in managerial or regulatory benchmarking. However, the robustness of maximum likelihood estimation of stochastic frontier models has not been thoroughly explored. This study examines the influence function of the estimator in a stochastic frontier context and derives sufficient conditions for robust maximum likelihood estimation based on the properties of error component distributions and copula density. It is found that the canonical distributional assumptions do not satisfy these conditions. The Student's t noise distribution shows attractive properties and can be paired with a broad range of inefficiency distributions while satisfying the conditions under independence. The parameter estimates and efficiency predictions from robust specifications are less sensitive to contaminating observations compared to non-robust specifications.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Matteo Zecchin, Sangwoo Park, Osvaldo Simeone, Marios Kountouris, David Gesbert
Summary: Standard Bayesian learning is suboptimal in generalization under misspecification and outliers. PAC-Bayes theory shows that the free energy criterion of Bayesian learning bounds the generalization error for Gibbs predictors under uncontaminated sampling distributions. This justifies the limitations of Bayesian learning in misspecified models and outliers. Recent work introduces PAC(m) bounds to enhance performance under misspecification, and this work proposes a robust free energy criterion combining the generalized logarithm score function with PAC(m) ensemble bounds, counteracting the effects of misspecification and outliers.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Operations Research & Management Science
Sally G. Arcidiacono, Damiano Rossello
Summary: Performance measurement is crucial in investment funds, and we propose an iterative methodology to select the most robust performance measure by quantifying statistical robustness and sensitivity to outliers for a set of eleven indices.
OPERATIONAL RESEARCH
(2022)
Article
Mathematics, Interdisciplinary Applications
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
Summary: This paper discusses global-local shrinkage priors for analyzing count data, providing sufficient conditions under which the posterior mean is unshrunk, and proposing tractable priors to satisfy those conditions, as well as a custom posterior computation algorithm without tuning parameters.
Article
Computer Science, Artificial Intelligence
Abdul Wahid, Dost Muhammad Khan, Ijaz Hussain, Sajjad Ahmad Khan, Zardad Khan
Summary: A novel robust unsupervised feature selection method, UFS-RDR, is proposed to improve feature selection performance by minimizing the graph regularized weighted data reconstruction error function, using Mahalanobis distance to detect outliers and determine Huber-type weight function. The experimental results show that UFS-RDR outperforms non-robust methods in the presence of contamination in unlabeled data.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Statistics & Probability
Yang Sun, Xiangzhong Fang
Summary: This article proposes a sparse estimator of the calibration parameters and its robust version based on adaptive lasso penalty, which adapts to the sample size of the physical experiments and the dimension of the calibration parameters. The proposed robust estimator efficiently handles heavy-tailed error and outliers, and the nonasymptotic properties of the estimators are investigated using concentration inequalities.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2022)
Article
Computer Science, Interdisciplinary Applications
M. A. O. Souza, H. S. Migon, J. B. M. Pereira
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2018)
Article
Operations Research & Management Science
Helio S. Migon, Larissa C. Alves
APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY
(2013)
Correction
Biology
T. C. O. Fonseca, M. A. R. Ferreira, H. S. Migon
Article
Statistics & Probability
Helio S. Migon, Alexandra M. Schmidt, Romy E. R. Ravines, Joao B. M. Pereira
COMPUTATIONAL STATISTICS
(2013)
Article
Computer Science, Interdisciplinary Applications
Celso Romulo Barbosa Cabral, Cibele Queiroz da-Silva, Helio S. Migon
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2014)
Article
Statistics & Probability
Vinicius P. Israel, Helio S. Migon
JOURNAL OF APPLIED STATISTICS
(2012)
Article
Computer Science, Interdisciplinary Applications
Edilberto Cepeda-Cuervo, Helio S. Migon, Liliana Garrido, Jorge A. Achcar
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
(2014)
Article
Statistics & Probability
Carlos A. Abanto-Valle, Helio S. Migon, Victor H. Lachos
BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS
(2012)
Article
Statistics & Probability
Glaura C. Franco, Helio S. Migon, Marcos O. Prates
BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS
(2019)
Article
Environmental Sciences
Roseane A. S. Albani, Vinicius V. L. Albani, Helio S. Migon, Antonio J. Silva Neto
Summary: This study addresses source characterization of atmospheric releases using adaptive strategies in Bayesian inference, numerical solution of the dispersion problem by a stabilized finite element method, and uncertainty quantification in measurements. The adaptive techniques accelerate the convergence of algorithms, leading to accurate reconstructions of source parameters. Results show errors in reconstructions ranging from 0.11% to 8.67% of the search region, with similar computational time compared to deterministic techniques.
ENVIRONMENTAL POLLUTION
(2021)
Article
Mathematics, Interdisciplinary Applications
Kelly C. M. Goncalves, Helio S. Migon, Leonardo S. Bastos
Article
Statistics & Probability
Cibele Queiroz Da-Silva, Helio S. Migon
REVSTAT-STATISTICAL JOURNAL
(2016)
Article
Social Sciences, Mathematical Methods
Kelly Cristina M. Goncalves, Fernando A. S. Moura, Helio S. Migon
SURVEY METHODOLOGY
(2014)
Proceedings Paper
Physics, Applied
Caio L. N. . Azevedo, Helio S. Migon
XI BRAZILIAN MEETING ON BAYESIAN STATISTICS (EBEB 2012)
(2012)
Article
Statistics & Probability
Esther Salazar, Marco A. R. Ferreira, Helio S. Migon
SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS
(2012)