Browsing by Author "Chatrabgoun, Omid"

Now showing 1 - 4 of 4
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    Approximation multivariate distribution of main indices of tehran stock exchange with pair-copula
    (College of Education, Wayne State University, 2013-11-01T00:00:00Z) Parham, Gholamali; Daneshkhah, Alireza; Chatrabgoun, Omid
    The multivariate distribution of five main indices of Tehran stock exchange is approximated using a pair-copula model. A vine graphical model is used to produce an n-dimensional copula. This is accomplished using a flexible copula called a minimum information (MI) copula as a part of pair-copula construction. Obtained results show that the achieved model has a good level of approximation.
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    Probabilistic modeling of financial uncertainties
    (IGI Global, 2018-04-30) Daneshkhah, Alireza; Hosseinian-Far, Amin; Chatrabgoun, Omid; Sedighi, Tabassom; Farsi, Maryam
    Since the global financial crash, one of the main trends in the financial engineering discipline has been to enhance the efficiency and flexibility of financial probabilistic risk assessments. Creditors could immensely benefit from such improvements in analysis hoping to minimise potential monetary losses. Analysis of real world financial scenarios require modeling of multiple uncertain quantities with a view to present more accurate, near future probabilistic predictions. Such predictions are essential for an informed decision making. In this article, the authors extend Bayesian Networks Pair-Copula Construction (BN-PCC) further using the minimum information vine model which results in a more flexible and efficient approach in modeling multivariate dependencies of heavy-tailed distribution and tail dependence as observed in the financial data. The authors demonstrate that the extended model based on minimum information Pair-Copula Construction (PCC) can approximate any non-Gaussian BN to any degree of approximation. The proposed method has been applied to the portfolio data derived from a Brazilian case study. The results show that the fitting of the multivariate distribution approximated using the proposed model has been improved compared to other previously published approaches.
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    Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model
    (Elsevier, 2016-06-21) Daneshkhah, Alireza; Remesan, Renji; Chatrabgoun, Omid; Holman, Ian P.
    This paper highlights the usefulness of the minimum information and parametric pair-copula construction (PCC) to model the joint distribution of flood event properties. Both of these models outperform other standard multivariate copula in modeling multivariate flood data that exhibiting complex patterns of dependence, particularly in the tails. In particular, the minimum information pair-copula model shows greater flexibility and produces better approximation of the joint probability density and corresponding measures have capability for effective hazard assessments. The study demonstrates that any multivariate density can be approximated to any degree of desired precision using minimum information pair-copula model and can be practically used for probabilistic flood hazard assessment.
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    Using machine learning algorithms to develop a clinical decision-making tool for COVID-19 inpatients
    (MDPI, 2021-06-09) Vepa, Abhinav; Saleem, Amer; Rakhshan, Kambiz; Daneshkhah, Alireza; Sedighi, Tabassom; Shohaimi, Shamarina; Omar, Amr; Salari, Nader; Chatrabgoun, Omid; Dharmaraj, Diana; Sami, Junaid; Parekh, Shital; Ibrahim, Mohamed; Raza, Mohammed; Kapila, Poonam; Chakrabarti, Prithwiraj
    Background: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Methods: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case–control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. Results: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.