In this paper we study the performance of the most popular bootstrap schemes for multilevel data. Also, we propose a modied version of the wild bootstrap procedure for hierarchical data structures. The wild bootstrap does not require homoscedasticity or assumptions on the distribution of the error processes. Hence, it is a valuable tool for robust inference in a multilevel framework. We assess the nite size performances of the schemes through a Monte Carlo study. The results show that for big sample sizes it always pays o to adopt an agnostic approach as the wild bootstrap outperforms other techniques.
Modugno L, Giannerini S (2015). The wild bootstrap for multilevel models. COMMUNICATIONS IN STATISTICS. THEORY AND METHODS, 44(22), 4812-4825 [10.1080/03610926.2013.802807].
The wild bootstrap for multilevel models
MODUGNO, LUCIA;GIANNERINI, SIMONE
2015
Abstract
In this paper we study the performance of the most popular bootstrap schemes for multilevel data. Also, we propose a modied version of the wild bootstrap procedure for hierarchical data structures. The wild bootstrap does not require homoscedasticity or assumptions on the distribution of the error processes. Hence, it is a valuable tool for robust inference in a multilevel framework. We assess the nite size performances of the schemes through a Monte Carlo study. The results show that for big sample sizes it always pays o to adopt an agnostic approach as the wild bootstrap outperforms other techniques.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
