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Accelerated degradation tests are used to provide accurate estimation of lifetime characteristics of highly reliable products within a relatively short testing time. Data from particular tests at high levels of stress (e.g., temperature,…

Applications · Statistics 2021-06-07 Helmi Shat , Rainer Schwabe

In recent years, more attention has been paid prominently to accelerated degradation testing in order to characterize accurate estimation of reliability properties for systems that are designed to work properly for years of even decades.…

Applications · Statistics 2021-09-23 Helmi Shat

Accelerated degradation tests are used to provide accurate estimation of lifetime properties of highly reliable products within a relatively short testing time. There data from particular tests at high levels of stress (e.\,g.\ temperature,…

Applications · Statistics 2021-10-13 Helmi Shat , Rainer Schwabe

Many highly reliable products are designed to function for years without failure. For such systems accelerated degradation testing may provide significance information about the reliability properties of the system. In this paper, we…

Applications · Statistics 2021-10-13 Helmi Shat , Rainer Schwabe

We propose a Bayesian framework for planning simple step-stress accelerated life tests when items are subject to two independent competing failure modes We assume that the competing risks are independent, with lifetimes following Weibull…

Methodology · Statistics 2026-04-13 Kiran Prajapat

Accelerated life testing (ALT) is a method of reducing the lifetime of components through exposure to extreme stress. This method of obtaining lifetime information involves the design of a testing experiment, i.e., an accelerated test plan.…

Applications · Statistics 2025-11-10 Owen McGrath , Kevin Burke

Accelerated degradation testing (ADT) is one of the major approaches in reliability engineering which allows accurate estimation of reliability characteristics of highly reliable systems within a relatively short time. The testing data are…

Applications · Statistics 2021-06-28 Helmi Shat , Norbert Gaffke

In the traditional simple step-stress partial accelerated life test (SSSPALT), the items are put on normal operating conditions up to a certain time and after that the stress is increased to get the failure time information early. However,…

Applications · Statistics 2024-08-02 Rathin Das , Biswabrata Pradhan

Stress is a complex issue with wide-ranging physical and psychological impacts on human daily performance. Specifically, acute stress detection is becoming a valuable application in contextual human understanding. Two common approaches to…

Machine Learning · Computer Science 2022-03-21 Van-Tu Ninh , Manh-Duy Nguyen , Sinéad Smyth , Minh-Triet Tran , Graham Healy , Binh T. Nguyen , Cathal Gurrin

Accelerated life tests (ALTs) play a crucial role in reliability analyses, providing lifetime estimates of highly reliable products. Among ALTs, step-stress design increases the stress level at predefined times, while maintaining a constant…

Statistics Theory · Mathematics 2024-02-12 Narayanaswamy Balakrishnan , María Jaenada , Leandro Pardo

One-shot devices data represent an extreme case of interval censoring.Some kind of one-shot units do not get destroyed when tested, and so, survival units can continue within the test providing extra information about their lifetime.…

Statistics Theory · Mathematics 2022-05-17 Narayanaswamy Balakrishnan , María Jaenada , Leandro Pardo

We propose a new stochastic optimization framework for empirical risk minimization problems such as those that arise in machine learning. The traditional approaches, such as (mini-batch) stochastic gradient descent (SGD), utilize an…

Machine Learning · Statistics 2020-02-04 Kenji Kawaguchi , Haihao Lu

In this article, we consider models for time-to-event data obtained from experiments in which stress levels are altered at intermediate stages during the observation period. These experiments, known as step-stress tests, belong to the…

Applications · Statistics 2018-07-04 Nandini Kannan , Debasis Kundu

In recent times, products have become increasingly complex and highly reliable, so failures typically occur after long periods of operation under normal conditions and may arise from multiple causes. This paper employs simple step-stress…

Methodology · Statistics 2025-08-01 Rathin Das , Soumya Roy , Biswabrata Pradhan

In this paper, we consider the situation under a life test, in which the failure time of the test units are not related deterministically to an observable stochastic time varying covariate. In such a case, the joint distribution of failure…

Statistics Theory · Mathematics 2014-06-18 S. Shemehsavar , Morteza Amini

One-shot devices analysis involves an extreme case of interval censoring, wherein one can only know whether the failure time is either before or after the test time. Some kind of one-shot devices do not get destroyed when tested, and so can…

Methodology · Statistics 2022-04-26 Narayanaswamy Balakrishnan , Elena Castilla , María Jaenada , Leandro Pardo

In this article we consider a simple step stress set up under the cumulative exposure model assumption. At each stress level the lifetime distribution of the experimental units are assumed to follow the generalized exponential distribution.…

Applications · Statistics 2017-07-18 Debashis Samanta , Debasis Kundu , Ayon Ganguly

This paper analytically investigates the optimal design of gamma degradation tests, including the number of test units, the number of inspections, and inspection times. We first derive optimal designs with periodic inspection times under…

Methodology · Statistics 2025-08-14 Hung-Ping Tung , Yu-Wen Chen

Accelerated life-tests (ALTs) are used for inferring lifetime characteristics of highly reliable products. In particular, step-stress ALTs increase the stress level at which units under test are subject at certain pre-fixed times, thus…

Statistics Theory · Mathematics 2024-02-12 Narayanaswamy Balakrishnan , Maria Jaenada , Leandro Pardo

The training of deep neural networks is inherently a nonconvex optimization problem, yet standard approaches such as stochastic gradient descent (SGD) require simultaneous updates to all parameters, often leading to unstable convergence and…

Machine Learning · Computer Science 2025-08-07 Chengcheng Yan , Jiawei Xu , Zheng Peng , Qingsong Wang
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