An extreme value prediction method based on clustering algorithm

Dai, Baorui; Xia, Ye; Li, Qi

doi:10.1016/j.ress.2022.108442

Cited by 19 publications

(8 citation statements)

References 43 publications

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“…To estimate the number of components for every parametric family type of mixture model, we have used standard model selection procedure [44]. The two criteria most commonly used in the literature [12,15,16,19], to name a few, to select mixture models are the Akaike information criterion (AIC) and the Bayesian information criterion BIC. We chose BIC for the following reason.…”

Section: Simulated Examplementioning

confidence: 99%

“…The simplest solution would be to separate the data related to different origination mechanisms and then use the simple parametric distribution [10,11]. However, this assumes that origination failure mechanisms and the optimal separation rule are known in advance, which is often not the case [4,7,12].…”

Section: Introductionmentioning

confidence: 99%

“…They provide a practical tool for approximating the underlying probability density function [15,19]. Also, they present a framework for identifying and classifying different subpopulations in a heterogeneous population [4,12]. In context of multiple failure data this is usually even more important as they can also provide optimal separation rule [4,12].…”

Section: Introductionmentioning

confidence: 99%

“…[19,26,27], https://doi.org/10.1016/j.ress.2022.108946 Received 19 July 2022; Received in revised form 25 October 2022; Accepted 28 October 2022 the state-of-the-art Gumbel mixture model parameter estimation algorithm is Expectation-Maximization (EM) [28]. The EM algorithm, is a popular tool for mixture modelling because it provides maximum likelihood parameter estimates [4,12,29]. The EM algorithm has also well-documented drawbacks [30].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gumbel mixture modelling for multiple failure data

Nagode

Oman²,

Klemenc³

et al. 2023

Reliability Engineering & System Safety

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Section: Simulated Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gumbel mixture modelling for multiple failure data

Nagode

Oman²,

Klemenc³

et al. 2023

Reliability Engineering & System Safety

View full text Add to dashboard Cite

“…Firstly, RMT (records management taskforce) was introduced into the analysis of the high-dimensional crosscorrelation matrix of the financial time series, thus creating a new method to study market correlation from the perspective of statistical physics [15]. e distribution of eigenvalues and eigenvectors of the cross-correlation matrix of the 406 stock market returns in the S&P500 stock market was investigated and compared with the random crosscorrelation matrix calculated with the same number and length of the random time series [16].…”

Section: Introductionmentioning

confidence: 99%

Art Financial Risk Prediction Algorithm Based on Random Matrix

Guo

2022

Mathematical Problems in Engineering

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Random matrix theory is applied in the financial field to study the correlation of the financial correlation coefficient matrix, which is a key factor in network construction. In this paper, the random matrix theory is combined with network construction to study the art financial risk prediction algorithm based on the random matrix. Based on the stochastic matrix theory and the key nodes of the network, the financial network and the “noise” network before and after the “denoising” are analyzed and compared. It is found that the key and important information of the original network is still retained after the “denoising” of the network, and the noise information corresponds to the information represented by the smaller nodes in the original network. Based on the stochastic matrix of artwork financial risk prediction, the topological properties of the financial network before and after denoising are analyzed and compared from the perspectives of minimum spanning tree, model, and community structure. Based on the random matrix theory, this paper discusses the financial correlation coefficient matrix and the statistical properties of the eigenvalues of the random matrix, and on this basis, the existing denoising methods are improved, the correlation coefficient matrix more suitable for constructing the financial network is established, and the art financial risk prediction algorithm is constructed. Then, based on the stochastic matrix theory and the key nodes of the network, the financial network and the noise network before and after denoising are analyzed and compared. It is found that the key and important information of the original network is still retained after denoising the network, and the noise information corresponds to the information represented by the relatively small nodes in the original network. Finally, based on the stochastic matrix of art financial risk prediction, analysis of the financial network topology, such as minimum spanning tree, model, and community structure, found that the improved financial network topology is more obvious and the structure is closer.

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