In this article, the most recent results in resampling methods in regression analysis are reviewed. Different classes of estimators are studied and compared the simple "jackknife" the weighted "jackknife" variance estimator for the case of heteroscedastic errors, the variable "jackknife", and the general "bootstrap" estimator. A brief description of Stein-rule estimators in the case of pre-testing estimation then follows. As an application, it is shown how to improve on pre-testing efficiency, following a Stein-rule estimator, by applying bootstrap methods to the estimated variances
The technique of jackknife is applied to a general class of estimators. Considering a natural popula...
We will study here different resampling procedures for creating confidence sets in linear models. A ...
We introduce a generalized bootstrap technique for estimators obtained by solving estimating equatio...
Abstract: In this paper, the hierarchical ways for building a regression model by using bootstrap an...
In an extension of the work of Liu and Singh (1992), we consider resampling estimates for the varian...
In an extension of the work of Liu and Singh (1992), we consider resampling estimates for the varian...
We present the resample package, which implements the resampling methods jackknife and Efron's boots...
This thesis presents the Jackknife Variance Estimator as a cost efficient alternative to the Bootstr...
Variance estimation techniques for nonlinear statistics, such as ratios and regression and correlati...
Not Availablea new boot~trap technique of variance estimation for complex survey data known as "Res...
AbstractB. Efron introducedjackknife-after-bootstrapas a computationally efficient method for estima...
This report compares the bootstrapping to jacknifing statistical procedures in terms in bias, confid...
This report compares the bootstrapping to jacknifing statistical procedures in terms in bias, confid...
In statistical inference, one is often interested in estimating the distribution of a root, which is...
The Bootstrap is the most widely used resampling statistical method. This method becomes very popula...
The technique of jackknife is applied to a general class of estimators. Considering a natural popula...
We will study here different resampling procedures for creating confidence sets in linear models. A ...
We introduce a generalized bootstrap technique for estimators obtained by solving estimating equatio...
Abstract: In this paper, the hierarchical ways for building a regression model by using bootstrap an...
In an extension of the work of Liu and Singh (1992), we consider resampling estimates for the varian...
In an extension of the work of Liu and Singh (1992), we consider resampling estimates for the varian...
We present the resample package, which implements the resampling methods jackknife and Efron's boots...
This thesis presents the Jackknife Variance Estimator as a cost efficient alternative to the Bootstr...
Variance estimation techniques for nonlinear statistics, such as ratios and regression and correlati...
Not Availablea new boot~trap technique of variance estimation for complex survey data known as "Res...
AbstractB. Efron introducedjackknife-after-bootstrapas a computationally efficient method for estima...
This report compares the bootstrapping to jacknifing statistical procedures in terms in bias, confid...
This report compares the bootstrapping to jacknifing statistical procedures in terms in bias, confid...
In statistical inference, one is often interested in estimating the distribution of a root, which is...
The Bootstrap is the most widely used resampling statistical method. This method becomes very popula...
The technique of jackknife is applied to a general class of estimators. Considering a natural popula...
We will study here different resampling procedures for creating confidence sets in linear models. A ...
We introduce a generalized bootstrap technique for estimators obtained by solving estimating equatio...