The Method of Solving Structural Reliability with Multiparameter Correlation Problem

Du, Juan; Li, Haibin; He, Yan

doi:10.1155/2017/6976301

Cited by 8 publications

(1 citation statement)

References 49 publications

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“…In this study, we systematically consider several copula families including the Gaussian, Frank, Clayton, Gumbel, and Joe families as well as their relevant rotated versions. The model with the best fit-complexity trade-off is selected using the AIC criterion [16]. Other popular copula families, such as the Farlie-Gumbel-Morgenstern (FGM) copula and the Ali-Mikhail-Haq (AMH) copula, have been discarded because they cannot achieve Fréchet bounds and can only accommodate relatively weak dependence between the margins.…”

Section: Copula-based Endogenous Switching Modelmentioning

confidence: 99%

Analysis of Household Consumption Behavior and Indebted Self-Selection Effects: Case Study of Thailand

Liu

Sirikanchanarak

Sriboonchitta

et al. 2018

Mathematical Problems in Engineering

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Copula is deemed to be a good approach for relaxing bivariate or multivariate distributions in econometric models. This paper combines static and dynamic copula functions with endogenous switching to study self-selection effects and interdependence between error terms. This technique, copula-based models, is applied to analyze household consumption behavior and indebted self-selection effects in Thailand. The independent, Gaussian, Frank, Clayton, Gumbel, and Joe copula functions and the relatively rotated copula functions were employed in the empirical work. The best model was selected by the information criterion, AIC. We separated the households into four groups, indebted households, debt-free households, households with housing/land loans, and households without housing/land loans, which favors the examination of the treatment effects of indebted households or households with housing debts. The main results indicate that dynamic copula-based models offer better performance than others, such as classical endogenous switching models or all static copula-based models. Also, the I–I and the G–G models underestimate the treatment effects relative to the best models. Additionally, importantly, the traditional normal bivariate distribution or the static copula function could characterize the relationship as regards errors between household debt choice and household consumption and can lead to very misleading implications about the treatment effects.

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Section: Copula-based Endogenous Switching Modelmentioning

confidence: 99%

Analysis of Household Consumption Behavior and Indebted Self-Selection Effects: Case Study of Thailand

Liu

Sirikanchanarak

Sriboonchitta

et al. 2018

Mathematical Problems in Engineering

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A neural network copula function approach for solving joint basic probability assignment in structural reliability analysis

Yang,

Sun,

et al. 2024

Quality & Reliability Eng

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Applying evidence theory to structural reliability analysis under epistemic uncertainty, it is necessary to consider the correlation of evidence variables. Among them, solving the joint basic probability assignment (BPA) of the evidence variables is a crucial link. In this study, a solution method of joint BPA based on neural network copula function is proposed. This method is to automatically construct copula function through neural network, which avoids the process of selecting the optimal copula function. Firstly, the neural network copula function is constructed based on the sample set of evidence variables. Then, the expression for solving the joint BPA using the neural network copula function is derived through vectors. Furthermore, the expression is used to map the marginal BPA of evidence variables to joint BPA, thus realizing the solution of joint BPA. Finally, the effectiveness of this method is verified by three examples. The results show that the neural network copula function describes the data distribution better than the optimal copula function selected by the traditional method. In addition, there is actually an error in solving the reliability intervals using the traditional optimal copula function method, whereas the results of this paper's neural network copula function method are more accurate and better for decision making.

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Time-dependent reliability calculation method of RC bridges based on the dual neural network

Yang

2023

Soft Comput

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The Method of Solving Structural Reliability with Multiparameter Correlation Problem

Cited by 8 publications

References 49 publications

Analysis of Household Consumption Behavior and Indebted Self-Selection Effects: Case Study of Thailand

Analysis of Household Consumption Behavior and Indebted Self-Selection Effects: Case Study of Thailand

A neural network copula function approach for solving joint basic probability assignment in structural reliability analysis

Time-dependent reliability calculation method of RC bridges based on the dual neural network

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