Conditional regression for single-index models

Bayesian quantile regression for single-index models

Using an asymmetric Laplace distribution, which provides a mechanism for Bayesian inference of quantile regression models, we develop a fully Bayesian approach to fitting single-index models in conditional quantile regression. In this work, we use a Gaussian process prior for the unknown nonparametric link function and a Laplace distribution on the index vector, with the latter motivated by the...

Conditional density estimation in a censored single-index regression model

Gaussian Process Single Index Models for Conditional Copulas

Parametric conditional copula models allow the copula parameters to vary with a set of covariates according to an unknown calibration function. In this paper we develop a flexible Bayesian method to estimate the calibration function of a bivariate conditional copula. We construct a prior distribution over the set of smooth calibration functions using a sparse Gaussian process (GP) prior for the...

Semiparametric efficiency for partially linear single-index regression models

We calculate semiparametric efficiency bounds for a partially linear single-index model using a simple method developed by [1]. We show that this model can be used to evaluate the efficiency of several existing estimators.

Semi-parametric single-index two-part regression models

In this paper, we proposed a semi-parametric single-index two-part regression model to weaken assumptions in parametric regression methods that were frequently used in the analysis of skewed data with additional zero values. The estimation procedure for the parameters of interest in the model was easily implemented. The proposed estimators were shown to be consistent and asymptotically normal. ...