Add to ORKGIn an extension of the work of Liu and Singh (1992), we consider resampling estimates for the variance of the least squares estimator in linear regression models. Second order terms in asymptotic expansions of these estimates are derived. By comparing the second order terms, certain generalised bootstrap schemes are seen to be theoretically better than other resampling techniques under very general conditions. The performance of the different resampling schemes are studied through a few simulations
In this paper, we have defined the biases and mean square errors of the two-phase sampling ratio and...
Variance estimation techniques for nonlinear statistics, such as ratios and regression and correlati...
Three re-sampling techniques are used to estimate the survival probabilities from an exponential lif...
In an extension of the work of Liu and Singh (1992), we consider resampling estimates for the varian...
Abstract: In this paper, the hierarchical ways for building a regression model by using bootstrap an...
In this article, the most recent results in resampling methods in regression analysis are reviewed. ...
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...
The bootstrap is a computationally intensive data analysis technique. It is particularly useful for ...
We compared general behaviour trends of resampling methods (bootstrap, bootstrap with Poisson distri...
We treat the problem of variance estimation of the least squares estimate of the parameter in high d...
In this simulation study, we compared ordinary least squares (OLS), weighted least squares (WLS), a...
This thesis presents the Jackknife Variance Estimator as a cost efficient alternative to the Bootstr...
In this paper, we have defined the biases and mean square errors of the two-phase sampling ratio and...
In this paper, we have defined the biases and mean square errors of the two-phase sampling ratio and...
In this paper, we have defined the biases and mean square errors of the two-phase sampling ratio and...
Variance estimation techniques for nonlinear statistics, such as ratios and regression and correlati...
Three re-sampling techniques are used to estimate the survival probabilities from an exponential lif...
In an extension of the work of Liu and Singh (1992), we consider resampling estimates for the varian...
Abstract: In this paper, the hierarchical ways for building a regression model by using bootstrap an...
In this article, the most recent results in resampling methods in regression analysis are reviewed. ...
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...
The bootstrap is a computationally intensive data analysis technique. It is particularly useful for ...
We compared general behaviour trends of resampling methods (bootstrap, bootstrap with Poisson distri...
We treat the problem of variance estimation of the least squares estimate of the parameter in high d...
In this simulation study, we compared ordinary least squares (OLS), weighted least squares (WLS), a...
This thesis presents the Jackknife Variance Estimator as a cost efficient alternative to the Bootstr...
In this paper, we have defined the biases and mean square errors of the two-phase sampling ratio and...
In this paper, we have defined the biases and mean square errors of the two-phase sampling ratio and...
In this paper, we have defined the biases and mean square errors of the two-phase sampling ratio and...
Variance estimation techniques for nonlinear statistics, such as ratios and regression and correlati...
Three re-sampling techniques are used to estimate the survival probabilities from an exponential lif...