In sampling finite populations, several resampling schemes have been proposed. The common starting point is that, despite its excellent asymptotic properties, Efron’s original bootstrap only works for i.i.d. data. This condition is not met in sampling finite populations, because of the dependence among units due to the sampling design. Hence, adaptations are needed to account for the non i.i.d. nature of data. Different versions of the standard bootstrap algorithm have been proposed in the literature. A new class of resampling procedures for finite populations is defined. Such a class appears to provide a unified framework that allows for encompassing other resampling algorithms already proposed. Its main theoretical justification is based ...
Traditional resampling methods for estimating sampling distributions sometimes fail, and alternative...
Motivated by the statistical inference problem in population genetics, we present a new sequential i...
In complex designs, classical bootstrap methods result in a biased variance estimator when the samp...
In sampling finite populations, several resampling schemes have been proposed. The common starting po...
In this paper, a class of resampling techniques for finite populations under πps sampling design is ...
In the present paper, resampling for finite populations under an iid sampling design is reviewed. Ou...
In this paper, a class of resampling techniques for finite populations under pps sampling design is ...
<p>The finite population bootstrap method is used as a computer-<br />intensive alternative to estim...
The aim of the paper is to study the problem of estimating the quantile function of a finite populat...
A resampling technique for probability-proportional-to size sampling designs is proposed. It is ess...
The aim of the paper is to study the problem of estimating the quantile function of a finite populati...
This paper examines resampling for bootstrap from a survey sampling point of view. Given an observed...
Consider a finite population $u$, which can be viewed as a realization of a superpopulation model. A...
Bootstrap is a useful tool for making statistical inference, but it may provide erroneous results un...
In this paper, resampling techniques based on pseudo-populations in the presence of a general πps sa...
Traditional resampling methods for estimating sampling distributions sometimes fail, and alternative...
Motivated by the statistical inference problem in population genetics, we present a new sequential i...
In complex designs, classical bootstrap methods result in a biased variance estimator when the samp...
In sampling finite populations, several resampling schemes have been proposed. The common starting po...
In this paper, a class of resampling techniques for finite populations under πps sampling design is ...
In the present paper, resampling for finite populations under an iid sampling design is reviewed. Ou...
In this paper, a class of resampling techniques for finite populations under pps sampling design is ...
<p>The finite population bootstrap method is used as a computer-<br />intensive alternative to estim...
The aim of the paper is to study the problem of estimating the quantile function of a finite populat...
A resampling technique for probability-proportional-to size sampling designs is proposed. It is ess...
The aim of the paper is to study the problem of estimating the quantile function of a finite populati...
This paper examines resampling for bootstrap from a survey sampling point of view. Given an observed...
Consider a finite population $u$, which can be viewed as a realization of a superpopulation model. A...
Bootstrap is a useful tool for making statistical inference, but it may provide erroneous results un...
In this paper, resampling techniques based on pseudo-populations in the presence of a general πps sa...
Traditional resampling methods for estimating sampling distributions sometimes fail, and alternative...
Motivated by the statistical inference problem in population genetics, we present a new sequential i...
In complex designs, classical bootstrap methods result in a biased variance estimator when the samp...