The nonparametric censored regression model is y = max[c, m(x) + e], where both the regression function m(x) and the … the derivatives of an uncensored nonparametric regression. We then estimate the regression function itself by solving the … usual estimators in uncensored nonparametric regression. We also provide root n estimates of weighted average derivatives of …

The nonparametric censored regression model, with a fixed, known censoring point (normalized to zero), is y = max[0,m … estimators of m(x) and its derivatives. The convergence rate is the same as for an uncensored nonparametric regression and its … heteroscedasticity. We also extend the estimator to the nonparametric truncated regression model, in which only uncensored data points …

semiparametric general trimmed estimator (GTE) of truncated and censored regression, which is highly robust but relatively imprecise …

estimation models. Parametric Engel curves are modelled using OLS, MM robust regression, and Tobit. Semiparametric Engel curves …

This paper proposes a semi-parametric approach to estimation in Tobit models. A generalized additive Tobit model of … presented here is non-parametric in two dimensions: first no distributional assumption is made for the error distribution, and … second, the demand equation is non-parametric with respect to price. We find that the elasticity of demand is substantially …

censored population. We then correct the derivative for the effects of the selection bias. We propose nonparametric and … semiparametric estimators for the derivative. As extensions, we discuss the cases of discrete regressors, measurement error in …

censored population. We then correct the derivative for the effects of the selection bias. We discuss nonparametric and … semiparametric estimators for the derivative. We also discuss the cases of discrete regressors and of endogenous regressors in both …