This report reviews several bootstrap methods with special emphasis on small sample properties. Only those bootstrap methods are covered which promise wide applicability. The small sample properties can be investigated analytically only in parametric bootstrap applications. Thus there is a strong emphasis on the latter although the bootstrap methods can be applied nonparametrically as well. The...

INTERVAL ESTIMATION OF EXCESS RISK RELATED EFFECTIVE DOSES IN TOBIT MODELS by Jia Wang ADVISOR: Professor Nan Lin December 2009 Saint Louis, Missouri In this thesis we consider interval estimation of excess risk related effective dose (ERED) in dose-response studies using tobit model. Let P (x) be the probability of response at dose level x. Considering the background probability P (0), excess ...

According to the IFCC, to determine the population-based reference interval (RI) of a test, 120 reference individuals are required. However, for some age groups such as newborns and preterm babies, it is difficult to obtain enough reference individuals. In this study, we consider both parametric and nonparametric bootstrap methods for estimating RIs and the associated confidence intervals (CIs)...

The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the joinpoint problem. In this paper, we propose a method of solving th...

1 The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the join-point problem. In this paper, we propose a method of solving...

An accurate estimate of the uncertainty associated with a parameter estimate is important if we want to avoid misleading inference. The bootstrap technique (Efron, 1979; Efron and Tisbshirani, 1993) is a very general way of measuring the accuracy of estimators, and was originally developed for parameter estimation given independent identically distributed (i.i.d.) data. However, random effects ...

In this talk, I present some theoretical and empirical properties of the uniform and biased-bootstrap for generalized method of moments (GMM) models. The version of the biased-bootstrap used in this paper is a form of weighted bootstrap with weights chosen to satisfy some constraints imposed by the model. A typical biased-bootstrap resample is obtained by resampling from a member within a pseud...

In this paper we propose a vectorized implementation of the non-parametric bootstrap for statistics based on sample moments. Basically, we adopt the multinomial sampling formulation of the non-parametric bootstrap, and compute bootstrap replications of sample moment statistics by simply weighting the observed data according to multinomial counts instead of evaluating the statistic on a resample...

8 Efron (1979) introduced the bootstrap method for independent data but it can not be easily applied to spatial data because of their dependency. For spatial data that are correlated in terms of their locations in the underlying space the moving block bootstrap method is usually used to estimate the precision measures of the estimators. The precision of the moving block bootstrap estimators is ...

8 Efron (1979) introduced the bootstrap method for independent data but it can not be easily applied to spatial data because of their dependency. For spatial data that are correlated in terms of their locations in the underlying space the moving block bootstrap method is usually used to estimate the precision measures of the estimators. The precision of the moving block bootstrap estimators is ...