background: prognostic models have clinical appeal to aid therapeutic decision making. two main practical challenges in development of such models are assessment of validity of models and imputation of missing data. in this study, importance of imputation of missing data and application of bootstrap technique in development, simplification, and assessment of internal validity of a prognostic mo...

This paper analyses the finite-sample and asymptotic properties of several bootstrap and m out of n bootstrap methods for constructing confidence interval (CI) endpoints in models defined by moment inequalities. In particular, we consider using these methods directly to construct CI endpoints. By considering two very simple models, the paper shows that neither the bootstrap nor the m out of n b...

Traditional inferential procedures based on the asymptotic normality assumption such as the Wald often produce misleading inferences when dealing with censored data and small samples. Alternative estimation techniques such as the jackknife and bootstrap percentile allow us to construct the interval estimates without relying on any classical assumptions. Recently, the double bootstrap became pre...

This paper considers the issue of bootstrap resampling in panel datasets. The availability of datasets with large temporal and cross sectional dimensions suggests the possibility of new resampling schemes. We suggest one possibility which has not been widely explored in the literature. It amounts to constructing bootstrap samples by resampling whole cross sectional units with replacement. In ca...

The bootstrap is typically much less reliable in the context of time-series models with serial correlation of unknown form than it is when regularity conditions for the conventional IID bootstrap, based on resampling, apply. It is therefore useful for practitioners to have available diagnostic techniques capable of evaluating bootstrap performance in specific cases. The techniques suggested in ...

In this paper we investigate bootstrap techniques applied to the estimation of the thinning parameters in INAR(p) models. We propose a new bootstrap approach based on sieve bootstrap. The approach is then applied to the Yule-Walker estimator of the thinning parameters. Monte Carlo experiments are carried out to valuate the performance of bootstrap estimator and the superiority of our proposal i...

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 delete-d 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 re...

This paper uses nonparametric methods to estimate the confidence intervals for the mean of asymmetric heavy tailed loss distributions. The nonparametric methods employed are the m out of n bootstrap, subsampling bootstrap, refined bootstrap, empirical likelihood ratio method, and bootstrap calibrated empirical likelihood methods. We evaluate the accuracy and compare the performance of the confi...

In this paper we study the consistency of different bootstrap procedures for constructing confidence intervals (CIs) for the unique jump discontinuity (change-point) in an otherwise smooth regression function in a stochastic design setting. This problem exhibits nonstandard asymptotics and we argue that the standard bootstrap procedures in regression fail to provide valid confidence intervals f...

In this paper we will investigate the consequences of applying the sieve bootstrap under regularity conditions that are sufficiently general to encompass both fractionally integrated and non-invertible processes. The sieve bootstrap is obtained by approximating the data generating process by an autoregression whose order h increases with the sample size T . The sieve bootstrap may be particular...