OLS estimator in a rank-rank regression. We show that the probability limits of these estimators may be too large or too …

random variable Y is median uncorrelated with a k-dimensional random vector X if and only if the slope from an LAD regression …

This paper is concerned with testing rationality restrictions using quantile regression methods. Specifically, we … unobservable. To analyze the economic restriction, we employ quantile regression methods because they allow us to utilize the … Regression …

This paper is concerned with the nonparametric estimation of regression quantiles where the response variable is … component functions in additive quantile regression models. -- Bahadur representation ; Censored data ; Kernel smoothing … ; Quantile regression ; Semiparametric models …

This paper considers inference in logistic regression models with high dimensional data. We propose new methods for … estimating and constructing confidence regions for a regression parameter of primary interest α0, a parameter in front of the … regression function, where s is smaller than n. Importantly, the estimators and these resulting confidence regions are "honest …

We develop uniformly valid confidence regions for regression coefficients in a high-dimensional sparse least absolute … deviation/median regression model. The setting is one where the number of regressors p could be large in comparison to the … sample size n, but only s << n of them are needed to accurately describe the regression function. Our new methods are based …