bias in the empirical distribution arising from the presence of noise. The leading bias in the empirical quantile function … mean and variance have been derived. Given a closed-form expression for the bias, bias-corrected estimator of the …-parametric and easy to implement. Our approach can be connected to corrections for selection bias and shrinkage estimation and is to …

bias arising from the presence of noise. Conditions are obtained under which this bias is asymptotically non … the density and quantile function. Our approach can be connected to corrections for selection bias and shrinkage …

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

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 …

have smaller bias that is flatter as a function of first step smoothing leading to improved small sample properties. Series …

bias and so are important when the first step is machine learning. We derive LR moment conditions for dynamic discrete …

Standard approaches to constructing nonparametric confidence bands for functions are frustrated by the impact of bias … overcome this problem it is common practice to either undersmooth, so as to reduce the impact of bias, or oversmooth, and … thereby introduce an explicit or implicit bias estimator. However, these approaches, and others based on nonstandard smoothing …

suffer from incidental parameter bias. We show how models with factor structures can also be applied to capture important …

parameter such that the bias of the estimator is negligible relative to its standard deviation. While this approach is … not clear what ratio of bias to standard deviation should be considered negligible. Second, since the bandwidth choice … address these issues, we construct valid confidence intervals that account for the presence of a nonnegligible bias and thus …