Robust Bayesian Regularized Estimation Based on t Regression Model by Zean Li, Weihua Zhao

“…The t distribution is a useful extension of the normal distribution, which can be used for statistical modeling of data sets with heavy tails, and provides robust estimation. …”
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An Extendable Python Implementation of Robust Optimization Monte Carlo by Vasilis Gkolemis, Michael Gutmann, Henri Pesonen

“…In this paper, we present the implementation of the LFI method robust optimization Monte Carlo (ROMC) in the Python package elfi. …”
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A Bernstein polynomial approach of the robust regression by Sihem Semmar, Omar Fetitah, Mohammed Kadi Attouch, Salah Khardani, Ibrahim M. Almanjahie

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Novel robust content-based watermarking for image by YANG Shi-yong1, WU Xiao-li2, YUE An-jun2, GE Jian-hua2

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Nonlinear robust precoding for coordinated multipoint transmission by Zhe-qi GU, Zhong-pei ZHANG

“…A nonlinear robust precoding algorithm was proposed,which redesigned the feedback matrix，the forward matrix and the scaling matrix of the traditional tomlinson-harashima precoding algorithm based on the statistical characteristics of the downlink channel state information errors.Simulation results show that the nonlinear robust precoding algorithm can achieve better performance than the traditional linear and nonlinear precoding algorithms when the downlink channel state information errors exist.Due to the different downlink channel state information errors between user equipments in the coordinated multi-point transmission,the traditional “best-first” ordering algorithm was invalid.So an improved ordering algorithm was proposed to reduce the average bit error rate of the nonlinear robust precoding algorithm.…”
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Applications of Mitscherlich Baule function: a robust regression approach by Rizwan Yousuf, Manish Sharma

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Robust Semiparametric Optimal Testing Procedure for Multiple Normal Means by Peng Liu, Chong Wang

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Statistical Language Learning in Children with Developmental Disorders by Ferhat KARAMAN

“…Developing a more comprehensive and holistic theoretical framework, examining statistical learning abilities throughout typical and atypical developmental stages, and establishing standardized methodologies and robust assessment tools can enhance our understanding of the relationship between developmental disorders and statistical learning. …”
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Robust Control, Optimization, and Applications to Markovian Jumping Systems by Shuping He, Zhengguang Wu, Hao Shen, Yanyan Yin, Quanxin Zhu

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A robust approach for the determination of Gurson model parameters by R. Sepe, G. Lamanna, F. Caputo

“…It must be pointed out that, even if the statistical character of some of the many physical parameters involved in the said model has been put in evidence, no serious attempt has been made insofar to link the corresponding statistic to the experimental and macroscopic results, as for example crack initiation time, material toughness, residual strength of the cracked component (R-Curve), and so on. …”
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A robust approach for the determination of Gurson model parameters by R. Sepe, G. Lamanna, F. Caputo

“…It must be pointed out that, even if the statistical character of some of the many physical parameters involved in the said model has been put in evidence, no serious attempt has been made insofar to link the corresponding statistic to the experimental and macroscopic results, as for example crack initiation time, material toughness, residual strength of the cracked component (R-Curve), and so on. …”
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Using Gaussian Processes for Metamodeling in Robust Optimization Problems by Claudemir Mota da Cruz, Fran Sérgio Lobato, Gustavo Barbosa Libotte

“…The results indicate that the proposed approach is effective in reducing the number of objective function evaluations required to obtain a robust solution, with no significant statistical differences in the quality of solutions achieved. …”
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Optimal network sizes for most robust Turing patterns by Hazlam S. Ahmad Shaberi, Aibek Kappassov, Antonio Matas-Gil, Robert G. Endres

“…To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties. Our analysis reveals that Turing patterns are more likely to occur by chance than previously thought and that the most robust Turing networks have an optimal size, consisting of only a handful of molecular species, thus significantly increasing their identifiability in biological systems. …”
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Assessing the robustness and implications of econometric estimates of climate sensitivity by Trude Storelvmo, Menghan Yuan, Thomas Leirvik, Kari Alterskjær, Peter C B Phillips, Chris Smith

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Economic freedom and growth dynamics in Indonesia: an empirical analysis of indicators driving sustainable development by Irsan Hardi, Mohd Afjal, Mohsin Khan, Ghalieb Mutig Idroes, Teuku Rizky Noviandy, Resty Tamara Utami

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Automated Classification of Glandular Tissue by Statistical Proximity Sampling by Jimmy C. Azar, Martin Simonsson, Ewert Bengtsson, Anders Hast

“…We circumvent this problem by an implicit representation that is both robust and highly descriptive, especially when combined with a multiple instance learning approach to image classification. …”
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Automatic Segmentation and Statistical Analysis of the Foveal Avascular Zone by Geanina Totolici, Mihaela Miron, Anisia-Luiza Culea-Florescu

“…This study facilitates the extraction of foveal avascular zone (FAZ) metrics from optical coherence tomography angiography (OCTA) images, offering valuable clinical insights and enabling detailed statistical analysis of FAZ size and shape across three patient groups: healthy, type II diabetes mellitus and both type II diabetes mellitus (DM) and high blood pressure (HBP). …”
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Statistical-Numerical Analysis for Pullout Tests of Ground Anchors by Juraj Chalmovský, Jan Štefaňák, Lumír Miča, Zdenek Kala, Šarūnas Skuodis, Arnoldas Norkus, Daiva Žilionienė

“…A linear regression model, employing the weighted least squares method and robust standard errors techniques were concluded to serve as a reliable statistical method suitable for achieving this goal. …”
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Testing Spherical Symmetry Based on Statistical Representative Points by Jiajuan Liang, Ping He, Qiong Liu

“…This paper introduces a novel chisquare test for spherical symmetry, utilizing statistical representative points. The proposed representative-point-based chisquare statistic is shown, through a Monte Carlo study, to considerably improve the power performance compared to the traditional equiprobable chisquare test in many high-dimensional cases. …”
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A scoping review of robustness concepts for machine learning in healthcare by Alan Balendran, Céline Beji, Florie Bouvier, Ottavio Khalifa, Theodoros Evgeniou, Philippe Ravaud, Raphaël Porcher

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