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...

Uniform resampling is the easiest to apply and is a general recipe for all problems, but it may require a large replication size B. To save computational effort in uniform resampling, balanced bootstrap resampling is proposed to change the bootstrap resampling plan. This resampling plan is effective for approximating the center of the bootstrap distribution. Therefore, this paper applies it to ...

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...

In this paper, we examine the validity of non-parametric spatial bootstrap as a procedure to quantify errors in estimates of N -point correlation functions. We do this by means of a small simulation study with simple point process models and estimating the two-point correlation functions and their errors. The coverage of confidence intervals obtained using bootstrap is compared with those obtai...

Euclidean distance -nearest neighbor ( -NN) classifiers are simple nonparametric classification rules. 5 5 Bootstrap methods, widely used for estimating the expected prediction error of classification rules, are motivated by the objective of calculating the ideal bootstrap estimate of expected prediction error. In practice, bootstrap methods use Monte Carlo resampling to estimate the ideal boot...

A number of nonparametric methods exist when studying the population and its parameters in the situation when the distribution is unknown. Some of them such as "resampling bootstrap method" are based on resampling from an initial sample. In this article empirical likelihood approach is introduced as a nonparametric method for more efficient use of auxiliary information to construct...

This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling only from cross-sectional units with replacement. Second, the temp...

Bootstrap resampling techniques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of functional ?(F), where F is random function. Efron’s Rubin’s bootstrap procedures extended, introducing an informative prior through Proper bootstrap. In this paper different techniques are used compared predictive classification regress...