Testing independence for Archimedean copula based on Bernstein estimate of Kendall distribution function

Susam, Selim Orhun; Ucer, Burcu Hudaverdi

doi:10.1080/00949655.2018.1478978

Cited by 14 publications

(9 citation statements)

References 12 publications

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“…In order to quantitatively analyse the weights calculated via the different methods, we tested the correlation of the corresponding secondary index weights using the nonparametric Kendall rank correlation coefficient (K). In particular, K is a rank-based correlation index that sorts two groups of sample datasets and describes the similarity between them [38]. is statistic is used to calculate the orderly correlation between two measurements.…”

Section: Comparison Of Weights Calculated Via Different Methodsmentioning

confidence: 99%

Construction Safety Risk Assessment of Large-Sized Deep Drainage Tunnel Projects

Wang

2021

Mathematical Problems in Engineering

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Frequent extreme climate events and rapid global urbanization have amplified the occurrence of accidents such as waterlogging or the overflow of pollution in big cities. This has increased the application scenarios of large-sized deep drainage tunnel projects (LSDDTPs). The scientific and accurate evaluation of the construction safety risks of LSDDTP can effectively reduce the corresponding economic losses and casualties. In this paper, we employed the hierarchical holographic model to construct the safety risk list of LSDDTPs in terms of the risk source and construction unit. Based on social network analysis, we then screened key indicators and calculated the weights of all secondary indicators from the correlation between risk factors. We subsequently developed a construction safety risk assessment model of LSDDTPs based on the matter-element extension method. The Donghu Deep Tunnel Project in Wuhan, China, was selected as a case study for the proposed method. The results of empirical research demonstrated that eight indicators (e.g., failure to effectively detect the change of the surrounding environment of the tunnel project) were key factors affecting the construction safety risk of IV, which is within the acceptable risk level. Our proposed model outperformed other methods (the fuzzy comprehensive evaluation, analytic hierarchy process, entropy weight method, and comprehensive weight method) in terms of scientific validity and research advancements.

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Section: Comparison Of Weights Calculated Via Different Methodsmentioning

confidence: 99%

Construction Safety Risk Assessment of Large-Sized Deep Drainage Tunnel Projects

Wang

2021

Mathematical Problems in Engineering

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“…Also, we note that Susam and Ucer [11] proposed estimation of K(t), which is a continuous approximation of K n (t). Genest et al [12] defined Kendall process…”

Section: Parameter Estimation Of Cotangent Copula: a Simulation Studymentioning

confidence: 99%

A New Family of Archimedean Copula via Trigonometric Generator Function

Susam

2020

Gazi University Journal of Science

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“…From Leblanc (2012) and Susam et al (2018), we know thatK m,n (t) is a consistent estimator. Also, Duchesne et al (1997) show thatθ CvM n is a consistent estimator.…”

Section: Minumum Cramér-von-mises Estimator Based On Kendall Distribumentioning

confidence: 99%

“…Susam et al (2018) defined the Bernstein estimator of order (m > 0) of the Kendall distribution function K as,…”

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confidence: 99%

Parameter Estimation of Some Archimedean Copulas Based on Minimum Cramér-von-Mises Distance

Susam

2020

JIRSS

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The purpose of this paper is to introduce a new estimation method for estimating the Archimedean copula dependence parameter in the non-parametric setting. The estimation of the dependence parameter has been selected as the value that minimizes the Cramér-von-Mises distance which measures the distance between Empirical Bernstein Kendall distribution function and true Kendall distribution function. A Monte Carlo study is performed to measure the performance of the new estimator and compared to conventional estimation methods. In terms of estimation performance, simulation results show that the proposed Minumum Cramér-von-Mises estimation method has a good performance for low dependence and a small sample size when compared with the other estimation methods. The new minimum distance estimation of the dependence parameter is applied to model the dependence of two real data sets.

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Testing independence for Archimedean copula based on Bernstein estimate of Kendall distribution function

Cited by 14 publications

References 12 publications

Construction Safety Risk Assessment of Large-Sized Deep Drainage Tunnel Projects

Construction Safety Risk Assessment of Large-Sized Deep Drainage Tunnel Projects

A New Family of Archimedean Copula via Trigonometric Generator Function

Parameter Estimation of Some Archimedean Copulas Based on Minimum Cramér-von-Mises Distance

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