Honest confidence intervals for the error variance in stepwise regression

Honest Confidence Intervals for the Error Variance in Stepwise Regression

An honest confidence interval for the error variance in a stepwise regression is a one-sided interval that adjusts for the effects of variable selection. The endpoint of this interval may be many times larger than the usual endpoint. Such adjustments are most important when selecting variables from a large number of available predictors, particularly in situations with more available predictors...

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...