bias. The procedure is equivalent to replacing the original kernel with a sequence of kernels constructed by convoluting … the original one in a specific way. In the nonparametric setting, we show that one can further reduce the bias by making … estimated. Furthermore, the semiparametric estimator based on such kernels enjoys the small bias property (SBP). Hence, the …

censored population. We then correct the derivative for the effects of the selection bias. We discuss nonparametric and …

Semiparametric correction for a sample selection bias in the presence of endogenous truncation is known to be much more … simple bandwidth-free semiparametric methodology to correct for a self-selection bias in a truncated sample, without any …

a semiparametric model. We show that both the variance part and the bias part of the nonparametric ingredient can lead … then propose two bias-robust inference procedures, based on multi-scale jackknife and analytical bias correction … functionals of the nonparametric ingredient. Simulation study shows that both bias-correction methods have good finite …

bias in the empirical distribution arising from the presence of noise. The leading bias in the empirical quantile function … for selection bias and shrinkage estimation and is to be contrasted with deconvolution. Simulation results confirm the …