Simple and Honest Confidence Intervals in Nonparametric Regression

SIMPLE AND HONEST CONFIDENCE INTERVALS IN NONPARAMETRIC REGRESSION By

We consider the problem of constructing honest confidence intervals (CIs) for a scalar parameter of interest, such as the regression discontinuity parameter, in nonparametric regression based on kernel or local polynomial estimators. To ensure that our CIs are honest, we derive and tabulate novel critical values that take into account the possible bias of the estimator upon which the CIs are ba...

Simple and Honest Confidence Intervals in Nonparametric Regression

We consider the problem of constructing honest confidence intervals (CIs) for a scalar parameter of interest, such as the regression discontinuity parameter, in nonparametric regression based on kernel or local polynomial estimators. To ensure that our CIs are honest, we derive and tabulate novel critical values that take into account the possible bias of the estimator upon which the CIs are ba...

Supplemental Appendix for SIMPLE AND HONEST CONFIDENCE INTERVALS IN NONPARAMETRIC REGRESSION

Supplemental Materials for “Simple and Honest Confidence Intervals in Nonparametric Regression”

We verify the conditions (1), (5) and (7) in some applications. Section C.1 shows that these conditions hold in the Gaussian white noise model under a mild extension of conditions in Donoho and Low (1992). Thus, the results apply to estimating, among other things, a function or one of its derivatives evaluated at a given point, when the function is observed in the white noise model. By equivale...

Confidence Intervals for Nonparametric Regression

In non-parametric function estimation, providing a confidence interval with the right coverage is a challenging problem. This is especially the case when the underlying function has a wide range of unknown degrees of smoothness. Here we propose two methods of constructing an average coverage confidence interval built from block shrinkage estimation methods. One is based on the James-Stein shrin...