This paper provides a regression approach to estimate tail dependence measures. The estimates coincide with the non-parametric estimates following Extreme Value Theory. The approach can easily be extended to higher dimensional analysis. We provide an example on international stock markets.

The aim of this paper is to show that measures on tail dependence can be estimated in a convenient way by regression analysis. This yields the same estimates as the non-parametric method within the multivariate Extreme Value Theory framework. The advantage of the regression approach is contained...

The objective of this paper is to compare the performance of two predictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled....

Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many...

Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on outcomes. The impact is described by the conditional quantile function and its functionals. In this paper we develop the nonparametric QR series framework, covering many regressors as a special...

Motivated by the fact that a linear speci fication in a quantile regression setting is unable to describe the non-linear relations among economic variables, as documented in the empirical econometrics literature, we are the first to formulate and analyze a multiple threshold quantile regression...

Economists have assumed that the Phillips curve, which shows a positive (negative) relation between inflation and the output ratio (unemployment rate), may be mapped off the aggregate demand -aggregate supply apparatus. The paper shows that the Phillips curve requires that unlikely restrictions...

Many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically censored least squares (SCLS) are sensitive to outliers and data contamination as we document. Therefore, we propose a semiparametric general trimmed estimator (GTE) of truncated...

We express the mean and variance terms in a double exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model, and whether they enter linearly or flexibly. When the variance term is null we obtain a...

For estimating regression function we can use many proceedings. In this paper, we have chosen to apply scaling functions to the estimation of regression functions. When one knows many bivariate date with the values of two variables, in the goal to express a correlation between the two variables...