This paper introduced a bootstrap method called truncated geometric bootstrap method for time series stationary process. We estimate the parameters of a geometric distribution which has been truncated as a probability model for the bootstrap algorithm. This probability model was used in resampling blocks of random length, where the length of each blocks has a truncated geometric distribution. The method was able to determine the block sizes b and probability p attached to its random selections. The mean and variance were estimated for the truncated geometric distribu-tion and the bootstrap algorithm developed based on the proposed probability model
SUMMARY The block bootstrap for time series consists in randomly resampling blocks of consecutive v...
Markov chains provide a flexible model for dependent random variables with appli-cations in such dis...
Abstract. The application of a parametric time series model to a water resources problem involves se...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
This article introduces a resampling procedure called the stationary bootstrap as a means of calcula...
The purpose of this paper is to introduce and examine two alternative, although similar, approaches ...
The Geometric distribution is one of the important distributions in the real life situation specifica...
We develop some asymptotic theory for applications of block bootstrap resampling schemes to multiva...
Abstract. We develop some asymptotic theory for applications of block bootstrap resampling schemes t...
The aim of this thesis is to investigate of bootstrap methods (Efron, 1979), in the the performance ...
It is common in parametric bootstrap to select the model from the data, and then treat as if it were...
Bootstrapping time series is one of the most acknowledged tools to study the statistical properties ...
This paper considers the block selection problem for a block bootstrap vari-ance estimator applied t...
We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, n...
SUMMARY The block bootstrap for time series consists in randomly resampling blocks of consecutive v...
Markov chains provide a flexible model for dependent random variables with appli-cations in such dis...
Abstract. The application of a parametric time series model to a water resources problem involves se...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
This article introduces a resampling procedure called the stationary bootstrap as a means of calcula...
The purpose of this paper is to introduce and examine two alternative, although similar, approaches ...
The Geometric distribution is one of the important distributions in the real life situation specifica...
We develop some asymptotic theory for applications of block bootstrap resampling schemes to multiva...
Abstract. We develop some asymptotic theory for applications of block bootstrap resampling schemes t...
The aim of this thesis is to investigate of bootstrap methods (Efron, 1979), in the the performance ...
It is common in parametric bootstrap to select the model from the data, and then treat as if it were...
Bootstrapping time series is one of the most acknowledged tools to study the statistical properties ...
This paper considers the block selection problem for a block bootstrap vari-ance estimator applied t...
We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, n...
SUMMARY The block bootstrap for time series consists in randomly resampling blocks of consecutive v...
Markov chains provide a flexible model for dependent random variables with appli-cations in such dis...
Abstract. The application of a parametric time series model to a water resources problem involves se...