one input and one output, and previous bootstrap methods proposed for inference have not been proven consistent, making … result is then used to prove that two different bootstrap procedures (one based on sub-sampling, the other based on smoothing …) provide consistent inference. The smooth bootstrap requires smoothing the irregularly-bounded density of inputs and outputs as …

the test statistics or using bootstrap methods to obtain critical values. Our simulation results indicate that our tests …

properties of several bootstrap-based inference procedures associated with a kernel-based estimator of density-weighted average … derivatives proposed by Powell, Stock, and Stoker (1989). In many cases, the validity of bootstrap-based inference procedures is … exception to this rule occurs for inference procedures involving a studentized estimator that employs a 'robust' variance …

one input and one output, and previous bootstrap methods proposed for inference have not been proven consistent, making … result is then used to prove that two different bootstrap procedures (one based on sub-sampling, the other based on smoothing …) provide consistent inference. The smooth bootstrap requires smoothing the irregularly-bounded density of inputs and outputs as …

This paper is concerned with bootstrap hypothesis testing in high dimensional linear regression models. Using a … theoretical framework recently introduced by Anatolyev (2012), we show that bootstrap F, LR and LM tests are asymptotically valid … the wild bootstrap in the presence of heteroskedasticity and to bootstrap methods for heavy tailed data. …