A two-stage symmetric regression quantile is considered as an alternative for estimating the population quantile for the simultaneous equations model. We introduce a two-stage symmetric trimmed least squares estimator (LSE) based on this quantile. It is shown that, under mixed multivariate normal errors, this trimmed LSE has asymptotic variance much closer to the Cramér-Rao lower bound than som...

In using multiple regression methods for prediction, one often considers the linear combination of explanatory variables as an index. Seeking a single such index when here are responses is rather more complicated. One classical approach to use coefficients from leading Canonical Correlation. However, based on variances unable disaggregate by quantile effects, lack robustness, and rely normal as...

The main idea of this paper is to study the dependence between the probability of default and the recovery rate on credit portfolio and to seek empirically this relationship. We examine the dependence between PD and RR by theoretical approach. For the empirically methodology, we use the bootstrapped quantile regression and the simultaneous quantile regression. These methods allow to determinate...

The purpose of regression analysis is to expose the relationship between a response variable and predictor variables. In real applications, the response variable cannot be predicted exactly from the predictor variables. Instead, the response for a fixed value of each predictor variable is a random variable. For this reason, we often summarize the behavior of the response for fixed values of the...

Interrupted Time Series (ITS) analysis represents a powerful quasi-experime-ntal design in which a discontinuity is enforced at a specific intervention point in a time series, and separate regression functions are fitted before and after the intervention point. Segmented linear/quantile regression can be used in ITS designs to isolate intervention effects by estimating the sudden/level change (...

DAGStat 2013-Freiburg Outline 1.

Quantile regresson extends classical least squares methods of estimating conditional mean functions by offering a variety of methods for estimating conditional quantile functions, thereby enabling the researcher to explore heterogeneous covariate effects. The course will offer a comprehensive introduction to quantile regression methods and survey some recent developments. The primary reference ...