A Look into the Problem of Preferential Sampling through the Lens of Survey Statistics

Vedensky, Daniel; Parker, Paul A.; Holan, Scott H.

doi:10.1080/00031305.2022.2143898

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…It is defined for small samples using pertinent statistical theory [52]. There are two working conditions in source domain data for Tasks 1, 3, and 5 of life prediction in Table 3, and the number of data groups is much less than 30, as is typical for small samples.…”

Section: Experiments Resultsmentioning

confidence: 99%

Research on Digital Twin Driven Rolling Bearing Model-Data Fusion Life Prediction Method

et al. 2023

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In industry, accurate remaining useful life (RUL) prediction is critical in improving system reliability and reducing downtime and accident risk. Numerous data-driven RUL prediction approaches have been proposed and achieved impressive performance in RUL prediction. However, most of them are still faced with the dilemma of limited samples, and most of popular transfer learning and domain adaptive methods adopt single-source domain adaptation (DA), ignoring the domain-shift within source domain and failing to fully utilize the multi-condition data. This article proposes a model-data fusion life prediction method based on digital twin (DT) and multi-source regression adversarial domain adaptation (MRADA) to address the aforementioned issues. For data-driven life prediction model, the model-based DT technology among them offers a significant amount of multi-condition training data. The proposed MRADA fully utilizes the benefits of DT simulation data by using intra-group alignment strategy, inter-group alignment strategy, adversarial learning, and regressor alignment strategy to learn domain-invariant features and supervision from multiple sources. The experimental findings demonstrate that the proposed fusion life prediction method can successfully address the issue of small samples and improve the accuracy of rolling bearing life prediction results.INDEX TERMS Digital twin, domain adaptation, remaining useful life prediction, small sample size.

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Section: Experiments Resultsmentioning

confidence: 99%

Research on Digital Twin Driven Rolling Bearing Model-Data Fusion Life Prediction Method

et al. 2023

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“…For the definition of small samples, this paper adopts the concept of small sample in statistics [67]. In the life prediction As can be seen from Fig.…”

Section: Verification Of Rul Prediction Resultsmentioning

confidence: 99%

Research on rolling bearing virtual-real fusion life prediction with digital twin

Zhao

Zhang

Fan

et al. 2023

Mechanical Systems and Signal Processing

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“…Applications and extensions of WP methods, summarized in Table 2 span multiple substantive domains, including sexual health (Bastos et al, 2018; Kunihama et al, 2016, 2019), environmental health (Vedensky et al, 2022), physical and mental health (Aliverti & Medicine, 2022; Parker et al, 2022; Parker & Holan, 2022; Trendtel & Robitzsch, 2021; Vedensky et al, 2022; Williams & Savitsky, 2021), poverty and housing (Fourrier‐Nicolai & Lubrano, 2020; Gunawan et al, 2020), and employment (Savitsky et al, 2022; Savitsky & Srivastava, 2018; Savitsky & Toth, 2016; Sun et al, 2022). Methodologically, applications and extensions of the method can be broadly classified into regression models, hierarchical models, mixture models, and computational and privacy guards.…”

Section: Weighted Pseudo‐likelihood For Complex Parametric Modelsmentioning

confidence: 99%

Bayesian estimation methods for survey data with potential applications to health disparities research

Wu,

Stephenson

2023

WIREs Computational Stats

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Understanding how and why certain communities bear a disproportionate burden of disease is challenging due to the scarcity of data on these communities. Surveys provide a useful avenue for accessing hard‐to‐reach populations, as many surveys specifically oversample understudied and vulnerable populations. When survey data is used for analysis, it is important to account for the complex survey design that gave rise to the data, to avoid biased conclusions. The field of Bayesian survey statistics aims to account for such survey design while leveraging the advantages of Bayesian models, which can flexibly handle sparsity through borrowing of information and provide a coherent inferential framework to easily obtain variances for complex models and data types. For these reasons, Bayesian survey methods seem uniquely well‐poised for health disparities research, where heterogeneity and sparsity are frequent considerations. This review discusses three main approaches found in the Bayesian survey methodology literature: (1) multilevel regression and post‐stratification, (2) weighted pseudolikelihood‐based methods, and (3) synthetic population generation. We discuss the advantages and disadvantages of each approach, examine recent applications and extensions, and consider how these approaches may be leveraged to improve research in population health equity.This article is categorized under: Applications of Computational Statistics > Clinical Trials Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Applications of Computational Statistics > Health and Medical Data/Informatics Statistical and Graphical Methods of Data Analysis > Sampling

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A Look into the Problem of Preferential Sampling through the Lens of Survey Statistics

Cited by 5 publications

References 33 publications

Research on Digital Twin Driven Rolling Bearing Model-Data Fusion Life Prediction Method

Research on Digital Twin Driven Rolling Bearing Model-Data Fusion Life Prediction Method

Research on rolling bearing virtual-real fusion life prediction with digital twin

Bayesian estimation methods for survey data with potential applications to health disparities research

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