Generating Synthetic Rainfall Total Using Multivariate Skew-t and Checkerboard Copula of Maximum Entropy

Radi, Noor Fadhilah Ahmad; Zakaria, Rosnah; Piantadosi, Julia; Boland, John; Zin, Wan Zawiah Wan; Azman, Muhammad Az-zuhri

doi:10.1007/s11269-017-1597-6

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…, 2021). In this research, the same Copula function was used to feed our model data (Ahmad Radi et al. , 2017; Hooshyaripor et al.…”

Section: Cbn Model Inference and Resultsmentioning

confidence: 99%

“…Under the framework of gradient boosting, this machine learning algorithm optimizes the value of the objective function (Feng et al, 2021;Yang et al, 2021). In this research, the same Copula function was used to feed our model data (Ahmad Radi et al, 2017;Hooshyaripor et al, 2014;Lee and Salas, 2011;Zhang and Baek, 2019) and approved the data with the K-S test (see Table 8). However, the problem with machine-learning models is that they cannot determine the Performance optimization of PPP projects causality of their outputs and are known as "black boxes" (Mannering et al, 2020).…”

Section: Xgboost and Shap Value Analysismentioning

confidence: 99%

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Evaluating and optimizing performance of public–private partnership projects using copula Bayesian network

Ghorbany

Yousefi

Noorzai

2022

ECAM

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PurposeBeing an efficient mechanism for the value of money, public–private partnership (PPP) is one of the most prominent approaches for infrastructure construction. Hence, many controversies about the performance effectiveness of these delivery systems have been debated. This research aims to develop a novel performance management perspective by revealing the causal effect of key performance indicators (KPIs) on PPP infrastructures.Design/methodology/approachThe literature review was used in this study to extract the PPPs KPIs. Experts’ judgment and interviews, as well as questionnaires, were designed to obtain data. Copula Bayesian network (CBN) has been selected to achieve the research purpose. CBN is one of the most potent tools in statistics for analyzing the causal relationship of different elements and considering their quantitive impact on each other. By utilizing this technique and using Python as one of the best programming languages, this research used machine learning methods, SHAP and XGBoost, to optimize the network.FindingsThe sensitivity analysis of the KPIs verified the causation importance in PPPs performance management. This study determined the causal structure of KPIs in PPP projects, assessed each indicator’s priority to performance, and found 7 of them as a critical cluster to optimize the network. These KPIs include innovation for financing, feasibility study, macro-environment impact, appropriate financing option, risk identification, allocation, sharing, and transfer, finance infrastructure, and compliance with the legal and regulatory framework.Practical implicationsIdentifying the most scenic indicators helps the private sector to allocate the limited resources more rationally and concentrate on the most influential parts of the project. It also provides the KPIs’ critical cluster that should be controlled and monitored closely by PPP project managers. Additionally, the public sector can evaluate the performance of the private sector more accurately. Finally, this research provides a comprehensive causal insight into the PPPs’ performance management that can be used to develop management systems in future research.Originality/valueFor the first time, this research proposes a model to determine the causal structure of KPIs in PPPs and indicate the importance of this insight. The developed innovative model identifies the KPIs’ behavior and takes a non-linear approach based on CBN and machine learning methods while providing valuable information for construction and performance managers to allocate resources more efficiently.

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“…, 2021). In this research, the same Copula function was used to feed our model data (Ahmad Radi et al. , 2017; Hooshyaripor et al.…”

Section: Cbn Model Inference and Resultsmentioning

confidence: 99%

Section: Xgboost and Shap Value Analysismentioning

confidence: 99%

Evaluating and optimizing performance of public–private partnership projects using copula Bayesian network

Ghorbany

Yousefi

Noorzai

2022

ECAM

View full text Add to dashboard Cite

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Polytopes of Discrete Copulas and Applications

Perrone

2022

Building Bridges Between Soft and Statistical Methodologies for Data Science

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Geometry of discrete copulas

Perrone

Solus

Uhler

2019

Journal of Multivariate Analysis

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Multivariate distributions are fundamental to modeling. Discrete copulas can be used to construct diverse multivariate joint distributions over random variables from estimated univariate marginals. The space of discrete copulas admits a representation as a convex polytope which can be exploited in entropy-copula methods relevant to hydrology and climatology. To allow for an extensive use of such methods in a wide range of applied fields, it is important to have a geometric representation of discrete copulas with desirable stochastic properties. In this paper, we show that the families of ultramodular discrete copulas and their generalization to convex discrete quasi-copulas admit representations as polytopes. We draw connections to the prominent Birkhoff polytope, alternating sign matrix polytope, and their most extensive generalizations in the discrete geometry literature. In doing so, we generalize some well-known results on these polytopes from both the statistics literature and the discrete geometry literature.

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Generating Synthetic Rainfall Total Using Multivariate Skew-t and Checkerboard Copula of Maximum Entropy

Cited by 5 publications

References 26 publications

Evaluating and optimizing performance of public–private partnership projects using copula Bayesian network

Evaluating and optimizing performance of public–private partnership projects using copula Bayesian network

Polytopes of Discrete Copulas and Applications

Geometry of discrete copulas

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