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copulas</title> <meta name="description" content="Search results for: copulas"> <meta name="keywords" content="copulas"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="copulas"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 11</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: copulas</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11</span> Parametric Inference of Elliptical and Archimedean Family of Copulas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alam%20Ali">Alam Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashok%20Kumar%20Pathak"> Ashok Kumar Pathak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, copulas have attracted significant attention for modeling multivariate observations, and the foremost feature of copula functions is that they give us the liberty to study the univariate marginal distributions and their joint behavior separately. The copula parameter apprehends the intrinsic dependence among the marginal variables, and it can be estimated using parametric, semiparametric, or nonparametric techniques. This work aims to compare the coverage rates between an Elliptical and an Archimedean family of copulas via a fully parametric estimation technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=elliptical%20copula" title="elliptical copula">elliptical copula</a>, <a href="https://publications.waset.org/abstracts/search?q=archimedean%20copula" title=" archimedean copula"> archimedean copula</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation" title=" estimation"> estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=coverage%20rate" title=" coverage rate"> coverage rate</a> </p> <a href="https://publications.waset.org/abstracts/171985/parametric-inference-of-elliptical-and-archimedean-family-of-copulas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171985.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">65</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10</span> Nonparametric Copula Approximations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serge%20Provost">Serge Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Yishan%20Zang"> Yishan Zang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Copulas are currently utilized in finance, reliability theory, machine learning, signal processing, geodesy, hydrology and biostatistics, among several other fields of scientific investigation. It follows from Sklar's theorem that the joint distribution function of a multidimensional random vector can be expressed in terms of its associated copula and marginals. Since marginal distributions can easily be determined by making use of a variety of techniques, we address the problem of securing the distribution of the copula. This will be done by using several approaches. For example, we will obtain bivariate least-squares approximations of the empirical copulas, modify the kernel density estimation technique and propose a criterion for selecting appropriate bandwidths, differentiate linearized empirical copulas, secure Bernstein polynomial approximations of suitable degrees, and apply a corollary to Sklar's result. Illustrative examples involving actual observations will be presented. The proposed methodologies will as well be applied to a sample generated from a known copula distribution in order to validate their effectiveness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=copulas" title="copulas">copulas</a>, <a href="https://publications.waset.org/abstracts/search?q=Bernstein%20polynomial%20approximation" title=" Bernstein polynomial approximation"> Bernstein polynomial approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=least-squares%20polynomial%20approximation" title=" least-squares polynomial approximation"> least-squares polynomial approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20density%20estimation" title=" kernel density estimation"> kernel density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=density%20approximation" title=" density approximation"> density approximation</a> </p> <a href="https://publications.waset.org/abstracts/170324/nonparametric-copula-approximations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170324.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9</span> Assessment Using Copulas of Simultaneous Damage to Multiple Buildings Due to Tsunamis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yo%20Fukutani">Yo Fukutani</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuji%20Moriguchi"> Shuji Moriguchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Takuma%20Kotani"> Takuma Kotani</a>, <a href="https://publications.waset.org/abstracts/search?q=Terada%20Kenjiro"> Terada Kenjiro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> If risk management of the assets owned by companies, risk assessment of real estate portfolio, and risk identification of the entire region are to be implemented, it is necessary to consider simultaneous damage to multiple buildings. In this research, the Sagami Trough earthquake tsunami that could have a significant effect on the Japanese capital region is focused on, and a method is proposed for simultaneous damage assessment using copulas that can take into consideration the correlation of tsunami depths and building damage between two sites. First, the tsunami inundation depths at two sites were simulated by using a nonlinear long-wave equation. The tsunamis were simulated by varying the slip amount (five cases) and the depths (five cases) for each of 10 sources of the Sagami Trough. For each source, the frequency distributions of the tsunami inundation depth were evaluated by using the response surface method. Then, Monte-Carlo simulation was conducted, and frequency distributions of tsunami inundation depth were evaluated at the target sites for all sources of the Sagami Trough. These are marginal distributions. Kendall’s tau for the tsunami inundation simulation at two sites was 0.83. Based on this value, the Gaussian copula, t-copula, Clayton copula, and Gumbel copula (n = 10,000) were generated. Then, the simultaneous distributions of the damage rate were evaluated using the marginal distributions and the copulas. For the correlation of the tsunami inundation depth at the two sites, the expected value hardly changed compared with the case of no correlation, but the damage rate of the ninety-ninth percentile value was approximately 2%, and the maximum value was approximately 6% when using the Gumbel copula. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=copulas" title="copulas">copulas</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte-Carlo%20simulation" title=" Monte-Carlo simulation"> Monte-Carlo simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20risk%20assessment" title=" probabilistic risk assessment"> probabilistic risk assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=tsunamis" title=" tsunamis"> tsunamis</a> </p> <a href="https://publications.waset.org/abstracts/103724/assessment-using-copulas-of-simultaneous-damage-to-multiple-buildings-due-to-tsunamis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103724.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">143</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> Modelling Operational Risk Using Extreme Value Theory and Skew t-Copulas via Bayesian Inference </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Betty%20Johanna%20Garzon%20Rozo">Betty Johanna Garzon Rozo</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Crook"> Jonathan Crook</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20Moreira"> Fernando Moreira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Operational risk losses are heavy tailed and are likely to be asymmetric and extremely dependent among business lines/event types. We propose a new methodology to assess, in a multivariate way, the asymmetry and extreme dependence between severity distributions, and to calculate the capital for Operational Risk. This methodology simultaneously uses (i) several parametric distributions and an alternative mix distribution (the Lognormal for the body of losses and the Generalized Pareto Distribution for the tail) via extreme value theory using SAS®, (ii) the multivariate skew t-copula applied for the first time for operational losses and (iii) Bayesian theory to estimate new n-dimensional skew t-copula models via Markov chain Monte Carlo (MCMC) simulation. This paper analyses a newly operational loss data set, SAS Global Operational Risk Data [SAS OpRisk], to model operational risk at international financial institutions. All the severity models are constructed in SAS® 9.2. We implement the procedure PROC SEVERITY and PROC NLMIXED. This paper focuses in describing this implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=operational%20risk" title="operational risk">operational risk</a>, <a href="https://publications.waset.org/abstracts/search?q=loss%20distribution%20approach" title=" loss distribution approach"> loss distribution approach</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=copulas" title=" copulas"> copulas</a> </p> <a href="https://publications.waset.org/abstracts/19385/modelling-operational-risk-using-extreme-value-theory-and-skew-t-copulas-via-bayesian-inference" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19385.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">603</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> Modelling Volatility Spillovers and Cross Hedging among Major Agricultural Commodity Futures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Roengchai%20Tansuchat">Roengchai Tansuchat</a>, <a href="https://publications.waset.org/abstracts/search?q=Woraphon%20Yamaka"> Woraphon Yamaka</a>, <a href="https://publications.waset.org/abstracts/search?q=Paravee%20Maneejuk"> Paravee Maneejuk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> From the past recent, the global financial crisis, economic instability, and large fluctuation in agricultural commodity price have led to increased concerns about the volatility transmission among them. The problem is further exacerbated by commodities volatility caused by other commodity price fluctuations, hence the decision on hedging strategy has become both costly and useless. Thus, this paper is conducted to analysis the volatility spillover effect among major agriculture including corn, soybeans, wheat and rice, to help the commodity suppliers hedge their portfolios, and manage the risk and co-volatility of them. We provide a switching regime approach to analyzing the issue of volatility spillovers in different economic conditions, namely upturn and downturn economic. In particular, we investigate relationships and volatility transmissions between these commodities in different economic conditions. We purposed a Copula-based multivariate Markov Switching GARCH model with two regimes that depend on an economic conditions and perform simulation study to check the accuracy of our proposed model. In this study, the correlation term in the cross-hedge ratio is obtained from six copula families – two elliptical copulas (Gaussian and Student-t) and four Archimedean copulas (Clayton, Gumbel, Frank, and Joe). We use one-step maximum likelihood estimation techniques to estimate our models and compare the performance of these copula using Akaike information criterion (AIC) and Bayesian information criteria (BIC). In the application study of agriculture commodities, the weekly data used are conducted from 4 January 2005 to 1 September 2016, covering 612 observations. The empirical results indicate that the volatility spillover effects among cereal futures are different, as response of different economic condition. In addition, the results of hedge effectiveness will also suggest the optimal cross hedge strategies in different economic condition especially upturn and downturn economic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agricultural%20commodity%20futures" title="agricultural commodity futures">agricultural commodity futures</a>, <a href="https://publications.waset.org/abstracts/search?q=cereal" title=" cereal"> cereal</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-hedge" title=" cross-hedge"> cross-hedge</a>, <a href="https://publications.waset.org/abstracts/search?q=spillover%20effect" title=" spillover effect"> spillover effect</a>, <a href="https://publications.waset.org/abstracts/search?q=switching%20regime%20approach" title=" switching regime approach"> switching regime approach</a> </p> <a href="https://publications.waset.org/abstracts/58830/modelling-volatility-spillovers-and-cross-hedging-among-major-agricultural-commodity-futures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58830.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">202</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> On Modeling Data Sets by Means of a Modified Saddlepoint Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serge%20B.%20Provost">Serge B. Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Yishan%20Zhang"> Yishan Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A moment-based adjustment to the saddlepoint approximation is introduced in the context of density estimation. First applied to univariate distributions, this methodology is extended to the bivariate case. It then entails estimating the density function associated with each marginal distribution by means of the saddlepoint approximation and applying a bivariate adjustment to the product of the resulting density estimates. The connection to the distribution of empirical copulas will be pointed out. As well, a novel approach is proposed for estimating the support of distribution. As these results solely rely on sample moments and empirical cumulant-generating functions, they are particularly well suited for modeling massive data sets. Several illustrative applications will be presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=empirical%20cumulant-generating%20function" title="empirical cumulant-generating function">empirical cumulant-generating function</a>, <a href="https://publications.waset.org/abstracts/search?q=endpoints%20identification" title=" endpoints identification"> endpoints identification</a>, <a href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation" title=" saddlepoint approximation"> saddlepoint approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=sample%20moments" title=" sample moments"> sample moments</a>, <a href="https://publications.waset.org/abstracts/search?q=density%20estimation" title=" density estimation"> density estimation</a> </p> <a href="https://publications.waset.org/abstracts/144553/on-modeling-data-sets-by-means-of-a-modified-saddlepoint-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144553.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Bivariate Time-to-Event Analysis with Copula-Based Cox Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Duhania%20O.%20Mahara">Duhania O. Mahara</a>, <a href="https://publications.waset.org/abstracts/search?q=Santi%20W.%20Purnami"> Santi W. Purnami</a>, <a href="https://publications.waset.org/abstracts/search?q=Aulia%20N.%20Fitria"> Aulia N. Fitria</a>, <a href="https://publications.waset.org/abstracts/search?q=Merissa%20N.%20Z.%20Wirontono"> Merissa N. Z. Wirontono</a>, <a href="https://publications.waset.org/abstracts/search?q=Revina%20Musfiroh"> Revina Musfiroh</a>, <a href="https://publications.waset.org/abstracts/search?q=Shofi%20Andari"> Shofi Andari</a>, <a href="https://publications.waset.org/abstracts/search?q=Sagiran%20Sagiran"> Sagiran Sagiran</a>, <a href="https://publications.waset.org/abstracts/search?q=Estiana%20Khoirunnisa"> Estiana Khoirunnisa</a>, <a href="https://publications.waset.org/abstracts/search?q=Wahyudi%20Widada"> Wahyudi Widada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For assessing interventions in numerous disease areas, the use of multiple time-to-event outcomes is common. An individual might experience two different events called bivariate time-to-event data, the events may be correlated because it come from the same subject and also influenced by individual characteristics. The bivariate time-to-event case can be applied by copula-based bivariate Cox survival model, using the Clayton and Frank copulas to analyze the dependence structure of each event and also the covariates effect. By applying this method to modeling the recurrent event infection of hemodialysis insertion on chronic kidney disease (CKD) patients, from the AIC and BIC values we find that the Clayton copula model was the best model with Kendall’s Tau is (τ=0,02). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bivariate%20cox" title="bivariate cox">bivariate cox</a>, <a href="https://publications.waset.org/abstracts/search?q=bivariate%20event" title=" bivariate event"> bivariate event</a>, <a href="https://publications.waset.org/abstracts/search?q=copula%20function" title=" copula function"> copula function</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20copula" title=" survival copula"> survival copula</a> </p> <a href="https://publications.waset.org/abstracts/179386/bivariate-time-to-event-analysis-with-copula-based-cox-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179386.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Estimation of the Upper Tail Dependence Coefficient for Insurance Loss Data Using an Empirical Copula-Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adrian%20O%27Hagan">Adrian O'Hagan</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20McLoughlin"> Robert McLoughlin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Considerable focus in the world of insurance risk quantification is placed on modeling loss values from lines of business (LOBs) that possess upper tail dependence. Copulas such as the Joe, Gumbel and Student-t copula may be used for this purpose. The copula structure imparts a desired level of tail dependence on the joint distribution of claims from the different LOBs. Alternatively, practitioners may possess historical or simulated data that already exhibit upper tail dependence, through the impact of catastrophe events such as hurricanes or earthquakes. In these circumstances, it is not desirable to induce additional upper tail dependence when modeling the joint distribution of the loss values from the individual LOBs. Instead, it is of interest to accurately assess the degree of tail dependence already present in the data. The empirical copula and its associated upper tail dependence coefficient are presented in this paper as robust, efficient means of achieving this goal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=empirical%20copula" title="empirical copula">empirical copula</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20events" title=" extreme events"> extreme events</a>, <a href="https://publications.waset.org/abstracts/search?q=insurance%20loss%20reserving" title=" insurance loss reserving"> insurance loss reserving</a>, <a href="https://publications.waset.org/abstracts/search?q=upper%20tail%20dependence%20coefficient" title=" upper tail dependence coefficient"> upper tail dependence coefficient</a> </p> <a href="https://publications.waset.org/abstracts/2645/estimation-of-the-upper-tail-dependence-coefficient-for-insurance-loss-data-using-an-empirical-copula-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2645.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Regression for Doubly Inflated Multivariate Poisson Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ishapathik%20Das">Ishapathik Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumen%20Sen"> Sumen Sen</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Rao%20Chaganty"> N. Rao Chaganty</a>, <a href="https://publications.waset.org/abstracts/search?q=Pooja%20Sengupta"> Pooja Sengupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dependent multivariate count data occur in several research studies. These data can be modeled by a multivariate Poisson or Negative binomial distribution constructed using copulas. However, when some of the counts are inflated, that is, the number of observations in some cells are much larger than other cells, then the copula based multivariate Poisson (or Negative binomial) distribution may not fit well and it is not an appropriate statistical model for the data. There is a need to modify or adjust the multivariate distribution to account for the inflated frequencies. In this article, we consider the situation where the frequencies of two cells are higher compared to the other cells, and develop a doubly inflated multivariate Poisson distribution function using multivariate Gaussian copula. We also discuss procedures for regression on covariates for the doubly inflated multivariate count data. For illustrating the proposed methodologies, we present a real data containing bivariate count observations with inflations in two cells. Several models and linear predictors with log link functions are considered, and we discuss maximum likelihood estimation to estimate unknown parameters of the models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=copula" title="copula">copula</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20copula" title=" Gaussian copula"> Gaussian copula</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20distributions" title=" multivariate distributions"> multivariate distributions</a>, <a href="https://publications.waset.org/abstracts/search?q=inflated%20distributios" title=" inflated distributios"> inflated distributios</a> </p> <a href="https://publications.waset.org/abstracts/105114/regression-for-doubly-inflated-multivariate-poisson-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105114.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> A Copula-Based Approach for the Assessment of Severity of Illness and Probability of Mortality: An Exploratory Study Applied to Intensive Care Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ainura%20Tursunalieva">Ainura Tursunalieva</a>, <a href="https://publications.waset.org/abstracts/search?q=Irene%20Hudson"> Irene Hudson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Continuous improvement of both the quality and safety of health care is an important goal in Australia and internationally. The intensive care unit (ICU) receives patients with a wide variety of and severity of illnesses. Accurately identifying patients at risk of developing complications or dying is crucial to increasing healthcare efficiency. Thus, it is essential for clinicians and researchers to have a robust framework capable of evaluating the risk profile of a patient. ICU scoring systems provide such a framework. The Acute Physiology and Chronic Health Evaluation III and the Simplified Acute Physiology Score II are ICU scoring systems frequently used for assessing the severity of acute illness. These scoring systems collect multiple risk factors for each patient including physiological measurements then render the assessment outcomes of individual risk factors into a single numerical value. A higher score is related to a more severe patient condition. Furthermore, the Mortality Probability Model II uses logistic regression based on independent risk factors to predict a patient’s probability of mortality. An important overlooked limitation of SAPS II and MPM II is that they do not, to date, include interaction terms between a patient’s vital signs. This is a prominent oversight as it is likely there is an interplay among vital signs. The co-existence of certain conditions may pose a greater health risk than when these conditions exist independently. One barrier to including such interaction terms in predictive models is the dimensionality issue as it becomes difficult to use variable selection. We propose an innovative scoring system which takes into account a dependence structure among patient’s vital signs, such as systolic and diastolic blood pressures, heart rate, pulse interval, and peripheral oxygen saturation. Copulas will capture the dependence among normally distributed and skewed variables as some of the vital sign distributions are skewed. The estimated dependence parameter will then be incorporated into the traditional scoring systems to adjust the points allocated for the individual vital sign measurements. The same dependence parameter will also be used to create an alternative copula-based model for predicting a patient’s probability of mortality. The new copula-based approach will accommodate not only a patient’s trajectories of vital signs but also the joint dependence probabilities among the vital signs. We hypothesise that this approach will produce more stable assessments and lead to more time efficient and accurate predictions. We will use two data sets: (1) 250 ICU patients admitted once to the Chui Regional Hospital (Kyrgyzstan) and (2) 37 ICU patients’ agitation-sedation profiles collected by the Hunter Medical Research Institute (Australia). Both the traditional scoring approach and our copula-based approach will be evaluated using the Brier score to indicate overall model performance, the concordance (or c) statistic to indicate the discriminative ability (or area under the receiver operating characteristic (ROC) curve), and goodness-of-fit statistics for calibration. We will also report discrimination and calibration values and establish visualization of the copulas and high dimensional regions of risk interrelating two or three vital signs in so-called higher dimensional ROCs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=copula" title="copula">copula</a>, <a href="https://publications.waset.org/abstracts/search?q=intensive%20unit%20scoring%20system" title=" intensive unit scoring system"> intensive unit scoring system</a>, <a href="https://publications.waset.org/abstracts/search?q=ROC%20curves" title=" ROC curves"> ROC curves</a>, <a href="https://publications.waset.org/abstracts/search?q=vital%20sign%20dependence" title=" vital sign dependence"> vital sign dependence</a> </p> <a href="https://publications.waset.org/abstracts/88209/a-copula-based-approach-for-the-assessment-of-severity-of-illness-and-probability-of-mortality-an-exploratory-study-applied-to-intensive-care-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88209.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">152</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Copula Autoregressive Methodology for Simulation of Solar Irradiance and Air Temperature Time Series for Solar Energy Forecasting </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andres%20F.%20Ramirez">Andres F. Ramirez</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20F.%20Valencia"> Carlos F. Valencia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing interest in renewable energies strategies application and the path for diminishing the use of carbon related energy sources have encouraged the development of novel strategies for integration of solar energy into the electricity network. A correct inclusion of the fluctuating energy output of a photovoltaic (PV) energy system into an electric grid requires improvements in the forecasting and simulation methodologies for solar energy potential, and the understanding not only of the mean value of the series but the associated underlying stochastic process. We present a methodology for synthetic generation of solar irradiance (shortwave flux) and air temperature bivariate time series based on copula functions to represent the cross-dependence and temporal structure of the data. We explore the advantages of using this nonlinear time series method over traditional approaches that use a transformation of the data to normal distributions as an intermediate step. The use of copulas gives flexibility to represent the serial variability of the real data on the simulation and allows having more control on the desired properties of the data. We use discrete zero mass density distributions to assess the nature of solar irradiance, alongside vector generalized linear models for the bivariate time series time dependent distributions. We found that the copula autoregressive methodology used, including the zero mass characteristics of the solar irradiance time series, generates a significant improvement over state of the art strategies. These results will help to better understand the fluctuating nature of solar energy forecasting, the underlying stochastic process, and quantify the potential of a photovoltaic (PV) energy generating system integration into a country electricity network. Experimental analysis and real data application substantiate the usage and convenience of the proposed methodology to forecast solar irradiance time series and solar energy across northern hemisphere, southern hemisphere, and equatorial zones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=copula%20autoregressive" title="copula autoregressive">copula autoregressive</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20irradiance%20forecasting" title=" solar irradiance forecasting"> solar irradiance forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20energy%20forecasting" title=" solar energy forecasting"> solar energy forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20generation" title=" time series generation"> time series generation</a> </p> <a href="https://publications.waset.org/abstracts/115914/copula-autoregressive-methodology-for-simulation-of-solar-irradiance-and-air-temperature-time-series-for-solar-energy-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115914.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">323</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th 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