Copulas in finance and insurance

Copulas provide a potential useful modeling tool to represent the dependence structure among variables and to generate joint distributions by combining given marginal distributions. Simulations play a relevant role in finance and insurance. They are used to replicate efficient frontiers or extremal values, to price options, to estimate joint risks, and so on. Using copulas, it is easy to construct and simulate from multivariate distributions based on almost any choice of marginals and any type of dependence structure. In this paper we outline recent contributions of statistical modeling using copulas in finance and insurance. We review issues related to the notion of copulas, copula families, copula-based dynamic and static dependence structure, copulas and latent factor models and simulation of copulas. Finally, we outline hot topics in copulas with a special focus on model selection and goodness-of-fit testing

Adjusted empirical likelihood estimation of the youden index and associated threshold for the bigamma model

The Youden index is a widely used measure in the framework of medical diagnostic, where the effectiveness of a biomarker (screening marker or predictor) for classifying a disease status is studied. When the biomarker is continuous, it is important to determine the threshold or cut-off point to be used in practice for the discrimination between diseased and healthy populations. We introduce a new method based on adjusted empirical likelihood for quantiles aimed to estimate the Youden index and its associated threshold. We also include bootstrap based confidence intervals for both of them. In the simulation study, we compare this method with a recent approach based on the delta method under the bigamma scenario. Finally, a real example of prostatic cancer, well known in the literature, is analyzed to provide the reader with a better understanding of the new metho

Adjusted empirical likelihood estimation of the youden index and associated threshold for the bigamia model

The Youden index is a widely used measure in the framework of medical diagnostic, where the effectiveness of a biomarker (screening marker or predictor) for classifying a disease status is studied. When the biomarker is continuous, it is important to determine the threshold or cut-off point to be used in practice for the discrimination between diseased and healthy populations. We introduce a new method based on adjusted empirical likelihood for quantiles aimed to estimate the Youden index and its associated threshold. We also include bootstrap based confidence intervals for both of them. In the simulation study, we compare this method with a recent approach based on the delta method under the bigamma scenario. Finally, a real example of prostatic cancer, well known in the literature, is analyzed to provide the reader with a better understanding of the new methodConfidence interval, Empirical likelihood, Optimal cut-off point, ROC curve, Youden index

Copulas in finance and insurance

Copulas provide a potential useful modeling tool to represent the dependence structure among variables and to generate joint distributions by combining given marginal distributions. Simulations play a relevant role in finance and insurance. They are used to replicate efficient frontiers or extremal values, to price options, to estimate joint risks, and so on. Using copulas, it is easy to construct and simulate from multivariate distributions based on almost any choice of marginals and any type of dependence structure. In this paper we outline recent contributions of statistical modeling using copulas in finance and insurance. We review issues related to the notion of copulas, copula families, copula-based dynamic and static dependence structure, copulas and latent factor models and simulation of copulas. Finally, we outline hot topics in copulas with a special focus on model selection and goodness-of-fit testing.Dependence structure, Extremal values, Copula modeling, Copula review

GsymPoint: An R Package to Estimate the Generalized Symmetry Point, an Optimal Cut-off Point for Binary Classification in Continuous Diagnostic Tests

In clinical practice, it is very useful to select an optimal cutpoint in the scale of a continuous biomarker or diagnostic test for classifying individuals as healthy or diseased. Several methods for choosing optimal cutpoints have been presented in the literature, depending on the ultimate goal. One of these methods, the generalized symmetry point, recently introduced, generalizes the symmetry point by incorporating the misclassification costs. Two statistical approaches have been proposed in the literature for estimating this optimal cutpoint and its associated sensitivity and specificity measures, a parametric method based on the generalized pivotal quantity and a nonparametric method based on empirical likelihood. In this paper, we introduce GsymPoint, an R package that implements these methods in a user-friendly environment, allowing the end-user to calculate the generalized symmetry point depending on the levels of certain categorical covariates. The practical use of this package is illustrated using three real biomedical datasetsThis research has been supported by several Grants from the Spanish Ministry of Science and Innovation. M. López-Ratón and C. Cadarso-Suárez acknowledge support to MTM2011-15849-E, MTM2011-28285-C02-00, MTM2014-52975-C2-1-R and MTM2015-69068-REDT. E.M. Molanes-López acknowledges support to MTM2011-28285-C02-02, ECO2011-25706, MTM2011-15849-E and MTM2015-69068-REDT. E. Letón acknowledges support to MTM2011-15849-E, MTM2011-28285-C02-02, PI13/02446 and MTM2015-69068-REDTS

Computer-Assisted Surgery Enables Beginner Surgeons, Under Expert Guidance, to Achieve Long-Term Clinical Results not Inferior to Those of a Skilled Surgeon in Knee Arthroplasty

Purpose: The purpose of this study is to determine whether the use of a surgical navigation system in total knee replacement (TKR) enables beginner and intermediate surgeons to achieve clinical PROM outcomes as good as those conducted by expert surgeons in the long term. Methods: We enrolled 100 consecutive patients whose total navigated knee arthroplasty (TKA) was performed in our institution from 2008 to 2010. According to the principal surgeon's surgical experience, the patients were divided into three groups: (1) beginner surgeons, with no more than 30 previous knee replacement performances, (2) intermediate surgeons, with more than 30 but not more than 300, and (3) expert surgeons, with more than 300 knee replacements. Demographic data collected on the cohort included gender, laterality, age, and body mass index (BMI). The outcome measures assessed were Forgotten Joint Score (FJS), implant positioning, limb alignment, and prosthesis survival rate. A margin of equivalence of±18.5 points in the FJS scale was prespecifed in terms of the minimal clinically important diference (MCID) to compare the FJS results obtained in the long period between the groups of interest. Results: The mean follow-up was 11.10±0.78, 10.86±0.66, and 11.30±0.74 years, respectively, for each of the groups. The long-term FJS mean score was 80.86±21.88, 81.36±23.87, and 90.48±14.65 for each group. The statistical analysis proved noninferiority and equivalence in terms of the FJS results reported in the long term by patients in Groups 1 or 2 compared to those in Group 3. More specifcally, it has been proved that the mean diference between groups is within the interval of equivalence defned in terms of the MCID. The overall prostheses survival rate was 93.7%. Conclusion: Navigated assisted TKA, under expert guidance, can be as efective when performed by beginner or intermediate surgeons as performed by senior surgeons regarding the accuracy of implant positioning, limb alignment, and long-term clinical outcome