BACKGROUND In prognostic studies, the lasso technique is attractive since it improves the quality of predictions by shrinking regression coefficients, compared to predictions based on a model fitted via unpenalized maximum likelihood. Since some coefficients are set to zero, parsimony is achieved as well. It is unclear whether the performance of a model fitted using the lasso still shows some o...

Nonparametric variance estimation for the distortion risk measure can be readily done through the bootstrap or the nonparametric delta method based on the influence function. The same task for the bootstrapped risk measures, however, has been relatively unexplored in the literature. In this paper we analytically derive the influence function of the exactly bootstrapped quantile and later extend...

In this enlightening and stimulating paper, Professors Romano, Shaikh, and Wolf construct two novel resampling-based multiple testing methods using the bootstrap and subsampling techniques and theoretically prove that these methods approximately control the FDR under weak regularity conditions. The theoretical results provide a satisfactory solution to an important and challenging problem in mu...

We consider two approaches for bias evaluation and reduction in the proportional hazards model (PHM) proposed by Cox. The rst one is an analytical approach in which we derive the n ?1 bias term of the maximum partial likelihood estimator. The second approach consists of resampling methods, namely the jackknife and the bootstrap. We compare all methods through a comprehensive set of Monte Carlo ...

We introduce a generalized bootstrap technique for estimators obtained by solving estimating equations. Some special cases of this generalized bootstrap are the classical bootstrap of Efron, the deleted jackknife and variations of the Bayesian bootstrap. The use of the proposed technique is discussed in some examples. Distributional consistency of the method is established and an asymptotic rep...

In this paper uniform confidence bands are constructed for nonparametric quantile estimates of regression functions. The method is based on the bootstrap, where resampling is done from a suitably estimated empirical density function (edf) for residuals. It is known that the approximation error for the uniform confidence band by the asymptotic Gumbel distribution is logarithmically slow. It is p...