In this work, higher order optimal window width is used to generate bootstrap kernel density likelihood. A simulated study is conducted to compare the distributions of the higher order bootstrap likelihoods with the exact (empirical) bootstrap likelihood. Our results indicate that the optimal window width of orders 2 and 4 perform better than those of higher orders. The higher order kernels (≥ 6 ) provided window widths, which obscured the details of the distribution when the exact bootstrap likelihood was taken to be the true density. Journal of the Nigerian Association of Mathematical Physics Vol. 8 2004: pp. 87-9
This thesis is composed in two parts. In the first chapter, we develop the theory of a novel fast bo...
Recently, an increasingly amount of literature focused on Bayesian computational methods to address...
The parametric bootstrap P-value based on a test statistic T is the exact tail probability of the ob...
This article unveils how the kernel block bootstrap method of Parente and Smith (2018a,2018b) can be...
In the context of functional estimation, the bootstrap approach amounts to substitution of the empir...
The bootstrap boosting algorithm is a bias reduction scheme. The adoption of higher-order Gaussian k...
Density estimation is the general approach adopted for the construction of an estimate of the underl...
We develop and implement a novel fast bootstrap for dependent data. Our scheme is based on the i.i.d...
Smoothed bootstrap method is a useful method to approximates the bias of Kernel density estimation. ...
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
[[abstract]]Variable window width kernel density estimators, with the width varying proportionally t...
Variable window width kernel density estimators, with the width varying proportionally to the square...
AbstractThis article considers a weighted bootstrap method to approximate the distribution of the ma...
The most frequent measure of phylogenetic uncertainty for splits is bootstrap support. Although larg...
summary:It has been known for a long time that for bootstrapping the distribution of the extremes un...
This thesis is composed in two parts. In the first chapter, we develop the theory of a novel fast bo...
Recently, an increasingly amount of literature focused on Bayesian computational methods to address...
The parametric bootstrap P-value based on a test statistic T is the exact tail probability of the ob...
This article unveils how the kernel block bootstrap method of Parente and Smith (2018a,2018b) can be...
In the context of functional estimation, the bootstrap approach amounts to substitution of the empir...
The bootstrap boosting algorithm is a bias reduction scheme. The adoption of higher-order Gaussian k...
Density estimation is the general approach adopted for the construction of an estimate of the underl...
We develop and implement a novel fast bootstrap for dependent data. Our scheme is based on the i.i.d...
Smoothed bootstrap method is a useful method to approximates the bias of Kernel density estimation. ...
International audienceThe bootstrap is a technique for performing statistical inference. The underly...
[[abstract]]Variable window width kernel density estimators, with the width varying proportionally t...
Variable window width kernel density estimators, with the width varying proportionally to the square...
AbstractThis article considers a weighted bootstrap method to approximate the distribution of the ma...
The most frequent measure of phylogenetic uncertainty for splits is bootstrap support. Although larg...
summary:It has been known for a long time that for bootstrapping the distribution of the extremes un...
This thesis is composed in two parts. In the first chapter, we develop the theory of a novel fast bo...
Recently, an increasingly amount of literature focused on Bayesian computational methods to address...
The parametric bootstrap P-value based on a test statistic T is the exact tail probability of the ob...