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This paper proposes a robust economic-statistical design for Hotelling's T2 control chart to monitor linear profiles, accounting for uncertain parameters. Unlike previous studies assuming fixed inputs, this work applies an uncertainty interval approach to address parameter uncertainty when investigating the T2 chart design. In this way the challenge of parameter uncertainty's adverse impact on the effectiveness of quality control models in practice is addressed. The key contribution of this study is formulating a T2 control chart that maintains accuracy despite uncertainty. Results demonstrate the value of increased costs to mitigate uncertainty risks. The proposed approach also facilitates selecting appropriate protection levels against uncertainty. The paper examines practical challenges and applications of implementing the T2 chart under real-world conditions affected by uncertainty. Overall, the proposed robust T2 control chart design incorporates parameter ranges for monitoring linear profiles, contrasting with previous models assuming fixed parameters. [ABSTRACT FROM AUTHOR]

Although statistical literacy has become a key competence in today's data-driven society, it is usually not a part of statistics education. To address this issue, we propose an innovative concept for a conference-like seminar on the topic of statistical literacy. This seminar draws attention to the relevance and importance of statistical literacy, and moreover, students are made aware of the process of science communication and are introduced to the peer review process for the assessment of scientific papers. In the summer term 2020, the seminar was conducted as a joint project by the University of Hamburg, the University of Muenster, and the Joachim Herz Foundation. In this article, we present the concept of the seminar and our experience with this concept in the summer term 2020.

The recent emergence and widespread adoption of Artificial Intelligence (AI) across various industries have not only increased efficiency and innovation but also introduced complex security challenges. Addressing these security challenges is an ever‐evolving area of research that seeks to mitigate issues affecting a wide range of individuals and sectors. Understanding the threats and vulnerabilities posed by these systems, and how to effectively defend against them, has become more crucial than ever. This paper aims to provide an overview of the most common attack vectors and their respective defense strategies, focusing particularly on those relevant to Industry 4.0. A major area of interest is adversarial machine learning, a relatively new field that focuses on corrupting, confusing, and manipulating AI models by intervening in different phases of their life cycle. The key findings indicate that FLAME offers the best protection against data poisoning attacks for neural network image detection models in a federated learning environment. Additionally, due to the specific threat model in Industry 4.0, defensive distillation emerges as the most promising defense strategy against evasion attacks. [ABSTRACT FROM AUTHOR]

Mathematical data science combines mathematical techniques with data science methods to extract insights from data. It involves working with data at all stages of the data life cycle and using various tools and techniques to analyze data. This field is applied in business, industry, and medicine to make predictions, identify patterns, and draw conclusions from data. The special issue of Mathematics presents ten papers that showcase innovative mathematical data science techniques in solving complex problems across different domains. These papers cover topics such as modeling failure times, time series analysis, process capability indices, control charts, internet usage monitoring, and more. The authors emphasize the importance of integrating advanced mathematical techniques with emerging technologies to drive innovation in data science. [Extracted from the article]

This document is an editorial introducing a special issue of the journal "Symmetry" on the topic of symmetrical and asymmetrical distributions in statistics and data science. The editorial provides a summary of the contributions to the special issue, which include studies on topics such as control charts, copulas, statistical distributions, discretization methods, spatial dependence, queuing models, survival analysis, and reliability analysis. The authors invite researchers to submit contributions to a second volume of the special issue, with a potential focus on the hypergeometric and negative hypergeometric distributions. [Extracted from the article]

In this paper, an exponential autoregressive model for complex time series data is presented. As for estimating the parameters of this nonlinear model, a three-step procedure based on quantile methods is proposed. This quantile-based estimation technique has the benefit of being more robust compared to least/absolute squares. The performance of the introduced exponential autoregressive model is evaluated by means of four established goodness-of-fit criteria. The practical utility of the novel time series model is showcased through a comparative analysis involving simulation studies and real-world data illustrations. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by L R fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as the left and right spreads of the unknown nonlinear fuzzy smooth function. In each stage, the nonparametric Nadaraya–Watson estimator is used to evaluate the center and the spreads of the fuzzy smooth function. A hybrid algorithm is proposed to estimate the unknown optimal bandwidths and autoregressive order simultaneously. Various goodness-of-fit measures are utilized for performance assessment of the fuzzy nonlinear kernel-based time series model and for comparative analysis. The practical applicability and superiority of the novel approach in comparison with further fuzzy time series models are demonstrated via a simulation study and some real-life applications. [ABSTRACT FROM AUTHOR]

In machine learning, the fused lasso is a regularization technique that is used to handle problems where the underlying signal has some kind of structure. In this paper, we extend fused lasso estimation for regression models characterized by fuzzy predictors and fuzzy responses. We present the first fused lasso regression model that is able to handle and analyze high-dimensional fuzzy data. The proposed model provides feature selection, leads to an improved predictive performance, and ensures interpretable and explainable models, all while being computationally efficient. Moreover, it can recover the true signal more accurately, find solutions that are structurally sparse, and is robust to noise. We conduct comprehensive comparative analysis and demonstrate the practical applicability of the presented fuzzy regression model through simulation and real-life applications. [ABSTRACT FROM AUTHOR]