Bootstrap and Wild Bootstrap for High Dimensional Linear Models

Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap

In regression models, appropriate bootstrap methods for inference robust to heteroskedasticity of unknown form are the wild bootstrap and the pairs bootstrap. The finite sample performance of a heteroskedastic-robust test is investigated with Monte Carlo experiments. The simulation results suggest that one specific version of the wild bootstrap outperforms the other versions of the wild bootstr...

The Wild Bootstrap ,

The Dependent Wild Bootstrap

We propose a new resampling procedure, the dependent wild bootstrap, for stationary time series. As a natural extension of the traditional wild bootstrap to time series setting, the dependent wild bootstrap offers a viable alternative to the existing block-based bootstrap methods, whose properties have been extensively studied over the last two decades. Unlike all of the block-based bootstrap m...

Bootstrap Model Selection in Generalized Linear Models

Model selection is a central component of data analysis Though there are a variety of methods for likelihood based estimation methods there are relatively few for non likelihood based generalized linear models GLM such as in the quasi likelihood and generalized es timating equation GEE approaches In this paper we develop basic and bias corrected bootstrap approaches to estimate the predictive m...

A Wild Bootstrap for Degenerate Kernel Tests

A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests that apply to random processes, for which the naive permutation-based bootstrap fails. It applies to a large group of kernel tests based on V-statistics, which are degenerate under the null hypothesis, and nondegener...