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Reliability analysis is a sub-field of uncertainty quantification that assesses the probability of a system performing as intended under various uncertainties. Traditionally, this analysis relies on deterministic models, where experiments…

Computation · Statistics 2026-05-19 Anderson V. Pires , Maliki Moustapha , Stefano Marelli , Bruno Sudret

This work deals with the stability analysis of nonlinear sampled-data systems under nonuniform sampling. It establishes novel relationships between the stability property of the exact discrete-time model for a given sequence of (aperiodic)…

Systems and Control · Electrical Eng. & Systems 2022-09-28 Alexis J. Vallarella , Hernan Haimovich

The coherent systems are basic concepts in reliability theory and survival analysis. They contain as particular cases the popular series, parallel and $k$-ou-of-$n$ systems (order statistics). Many results have been obtained for them by…

Statistics Theory · Mathematics 2024-12-13 Jorge Navarro , Julio Mulero

This paper proposes a class of resilient state estimators for LTV discrete-time systems. The dynamic equation of the system is assumed to be affected by a bounded process noise. As to the available measurements, they are potentially…

Systems and Control · Electrical Eng. & Systems 2024-09-23 Alexandre Kircher , Laurent Bako , Eric Blanco , Mohamed Benallouch

In this paper, an attack-resilient estimation algorithm is presented for linear discrete-time stochastic systems with state and input constraints. It is shown that the state estimation errors of the proposed estimation algorithm are…

Optimization and Control · Mathematics 2019-03-21 Wenbin Wan , Hunmin Kim , Naira Hovakimyan , Petros G. Voulgaris

This paper deals with the state estimation problem in discrete-event systems modeled with nondeterministic finite automata, partially observed via a sensor measuring unit whose measurements (reported observations) may be vitiated by a…

Information Theory · Computer Science 2020-11-04 Yuting Li , Christoforos N. Hadjicostis , Naiqi Wu , Zhiwu Li

Latent diffusion models (LDMs) achieve state-of-the-art performance across various tasks, including image generation and video synthesis. However, they generally lack robustness, a limitation that remains not fully explored in current…

Machine Learning · Computer Science 2025-06-10 Boris Martirosyan , Alexey Karmanov

Progressive multi-state survival outcomes are common in trials with recurrent or sequential events and require treatment effect estimands that remain interpretable without proportional intensity or Markov assumptions. The restricted mean…

Methodology · Statistics 2026-01-22 Xi Fang , Bingkai Wang , Guangyu Tong , Liangyuan Hu , Shuangge Ma , Fan Li

The log-normal distribution is one of the most common distributions used for modeling skewed and positive data. It frequently arises in many disciplines of science, specially in the biological and medical sciences. The statistical analysis…

Methodology · Statistics 2020-01-01 Ayanendranath Basu , Abhijit Mandal , Nirian Martin , Leandro Pardo

In reliability and life testing when the exponentially distributed components are put in series, it is generally assumed that the lifetimes of the components are independently distributed, which leads to some errors if they are not actually…

Statistics Theory · Mathematics 2019-02-26 Asok K. Nanda , Sanjib Gayen , Shovan Chowdhury

The literature on provable robustness in machine learning has primarily focused on static prediction problems, such as image classification, in which input samples are assumed to be independent and model performance is measured as an…

Machine Learning · Computer Science 2023-03-30 Aounon Kumar , Vinu Sankar Sadasivan , Soheil Feizi

This paper studies the identification, estimation, and hypothesis testing problem in complete and incomplete economic models with testable assumptions. Testable assumptions ($A$) give strong and interpretable empirical content to the models…

Econometrics · Economics 2022-03-11 Moyu Liao

Autoregressive models are a class of time series models that are important in both applied and theoretical statistics. Typically, inferential devices such as confidence sets and hypothesis tests for time series models require nuanced…

Statistics Theory · Mathematics 2022-01-19 Hien Duy Nguyen

Many cases exist in which a black-box function $f$ with high evaluation cost depends on two types of variables $\bm x$ and $\bm w$, where $\bm x$ is a controllable \emph{design} variable and $\bm w$ are uncontrollable \emph{environmental}…

Machine Learning · Statistics 2021-02-09 Yu Inatsu , Shogo Iwazaki , Ichiro Takeuchi

Survival extropy, which quantifies the uncertainty associated with the remaining lifetime distribution, provides an information-theoretic perspective on survival behavior. We consider a divergence measure based on survival extropy and…

Statistics Theory · Mathematics 2025-12-03 Naresh Garg , Isha Dewan , Sudheesh Kumar Kattumannil

This paper presents robust inference methods for general linear hypotheses in linear panel data models with latent group structure in the coefficients. We employ a selective conditional inference approach, deriving the conditional…

Econometrics · Economics 2025-11-25 Oguzhan Akgun , Ryo Okui

Combined experiments and computational modelling are used to increase understanding of the suitability of the Single-Edge Notch Tension (SENT) test for assessing hydrogen embrittlement susceptibility. The SENT tests were designed to provide…

Computational Engineering, Finance, and Science · Computer Science 2024-04-30 L. Cupertino-Malheiros , T. K. Mandal , F. Thebault , E. Martínez-Pañeda

Semiconductors are widely used in various applications and critical infrastructures. These devices have specified lifetimes and quality targets that manufacturers must achieve. Lifetime estimation is conducted through accelerated stress…

Applications · Statistics 2025-01-14 Lukas Sommeregger , Jürgen Pilz

A test of the null hypothesis that a hazard rate is monotone nondecreasing, versus the alternative that it is not, is proposed. Both the test statistic and the means of calibrating it are new. Unlike previous approaches, neither is based on…

Statistics Theory · Mathematics 2007-06-13 Peter Hall , Ingrid Van Keilegom

This paper develops robust confidence intervals in high-dimensional and left-censored regression. Type-I censored regression models are extremely common in practice, where a competing event makes the variable of interest unobservable.…

Statistics Theory · Mathematics 2017-08-16 Jelena Bradic , Jiaqi Guo
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