Calibrated Percentile Double Bootstrap For Robust Linear Regression Inference

Understand

We consider inference for the parameters of a linear model when the covariates are random and the relationship between response and covariates is possibly non-linear.

• Conventional inference methods such as z-intervals perform poorly in these cases.
• We propose a double bootstrap-based calibrated percentile method, perc-cal, as a general-purpose CI method which performs very well relative to alternative methods in challenging situations such as these.
• The superior performance of perc-cal is demonstrated by a thorough, full-factorial design synthetic data study as well as a real data example involving the length of criminal sentences.