On concomitants of upper record statistics and survival analysis for a pseudo-Gompertz distribution

Gebizlioglu, Omer/0000-0002-3824-281XWOS: 000325665900017This paper presents upper record statistics and their concomitants for a bivariate pseudo-Gompertz distribution about paired lifetime variables. Survival and hazard functions are derived for the distribution. The survival and hazard functions are displayed for some selected values of the parameters of concern. Interpretations are given for the potential reliability and actuarial applications of the obtained results. (C) 2013 Elsevier B.V. All rights reserved

Tolerance intervals for quantiles of bivariate risks and risk measurement

This paper considers joint distributions of order statistics for risk variables and their concomitants for actuarial risk analysis under dependence. With this purpose, bivariate integral transformations are performed and some examples are presented using copulas, the FGM copulas in particular. Quantiles of the distributions concerned are discussed and their tolerance intervals are constructed. Risk measures such as VaR in the set up of the tolerance intervals are included in the discussions.

Bivariate Pseudo-Gompertz distribution and concomitants of its order statistics

This paper presents a new bivariate Pseudo-Gompertz distribution that sprouts from the classical Gompertz distribution and possesses the features of pseudo-distribution functions. In addition to some standard properties of the proposed distribution, distributions of order statistics and their concomitants for samples drawn from the new distribution are obtained. The survival and hazard functions of the concomitants are shown and their values are tabled. Interpretations of the results are given in connection with risk events and risk management. (C) 2013 Elsevier B.V. All rights reserved

Determination of dependency parameter in joint distribution of dependent risks by fuzzy approach

Tank, Fatih/0000-0003-3758-396X; Gebizlioglu, Omer/0000-0002-3824-281XWOS: 000235491400013Frechet bounds for distribution of sum of dependent risks and Farlie-Gumbel-Morgenstein (FOM) distribution family are suggested for the analysis of dependent risks. Behaviour of the coefficient of dependency in FGM distribution is investigated by fuzzy approach. Deliberations are made for the dependency and uncertainty context. (c) 2005 Elsevier B.V. All fights reserved

A Pseudo-Pareto Distribution and Concomitants of Its Order Statistics

7th International Workshop on Applied Probability (IWAP) -- JUN 16-19, 2014 -- Antalya, TURKEYGebizlioglu, Omer/0000-0002-3824-281XWOS: 000387113000008Pareto distributions are very flexible probability models with various forms and kinds. In this paper, a new bivariate Pseudo-Pareto distribution and its properties are presented and discussed. Main variables, order statistics and concomitants of this distribution are studied and their importance for risk and reliability analysis is explained. Joint and marginal distributions, complementing cumulative distributions and hazard functions of the variables are derived. Numerical illustrations, graphical displays and interpretations for the obtained distributions and derived functions are provided. An implementation example on defaultable bonds is performed

Measurement of bivariate risks by the north?south quantile points approach

This paper attempts to determine the Value at Risk (VaR) and Conditional Value at Risk (CVaR) measures for the sum of bivariate risks under dependence. The computation of these risk measures is performed by the north?south quantile points of bivariate distributions. The Farlie?Gumbel?Morgenstern (FGM) copula model is chosen to express dependence of bivariate risks. The behaviors of VaR and CVaR are examined by varying dependence parameter values of the copula model and probability levels of the risk measures. The findings are interpreted from the view point of portfolio risk management

The psychological disconnect between knowing how to live healthy and actually pursuing a healthy lifestyle

A survey of Rutgers University students showing that obesity prevalence is not due to lack of knowledge but a lack of action.Spring 201