Estimation of single-index quantile regression Model

Efficient combination of conditional quantile information for the single-index model

A local-linear estimator —called a quantile averaging estimator — is proposed for estimation of a single-index model, where an important restriction is placed on the distribution of the error term: that its distribution is a location-scale model. The estimator could be called a semiparametric L-estimator, mixing together information provided by separate single-index quantile regressions in a we...

A Single-index Quantile Regression Model and Its Estimation

Inference for Single-index Quantile Regression Models with Profile Optimization by Shujie Ma

Single index models offer greater flexibility in data analysis than linear models but retain some of the desirable properties such as the interpretability of the coefficients. We consider a pseudo-profile likelihood approach to estimation and testing for single-index quantile regression models. We establish the asymptotic normality of the index coefficient estimator as well as the optimal conve...

Variable Selection in Single Index Quantile Regression for Heteroscedastic Data

Quantile regression (QR) has become a popular method of data analysis, especially when the error term is heteroscedastic, due to its relevance in many scientific studies. The ubiquity of high dimensional data has led to a number of variable selection methods for linear/nonlinear QR models and, recently, for the single index quantile regression (SIQR) model. We propose a new algorithm for simult...

Quantile Estimation of A general Single-Index Model

Regression quantiles, along with the dual methods of regression rank scores, can be considered one of the major statistical breakthroughs of the past decades. Its advantages over the other estimation methods have been well investigated. Regression quantile methods provide a much more complete statistical analysis of the stochastic relationships among variables; in addition, they are more robust...