In this article, we describe jackknife2, a new prefix command for jackknifing linear estimators. It takes full advantage of the available leave-one-out formula, thereby allowing for substantial reduction in computing time. Of special note is that jackknife2 allows the user to compute cross-validation and diagnostic measures that are currently not available after ivregress 2sls, xtreg, and xtivregress
Quenouille has developed a procedure, later termed the jackknife by Tukey, for reducing the bias of ...
We propose jackknife estimators for nonlinear dynamic panel data models with fixed effects that redu...
In this study, we adapt sufficient bootstrap into the jackknife-after-bootstrap (JaB) algorithm. The...
In this article, we describe jackknife2, a new prefix command for jackknifing linear estimators. It ...
The error or variability of machine learning algorithms is often assessed by repeatedly refitting a ...
In this paper, the delete-mj jackknife estimator is proposed. This estimator is based on samples obt...
In this paper, the delete-mj jackknife estimator is proposed. This estimator is based on samples obt...
This paper establishes estimators of the truncation point of continuous distribution using the jackk...
The assumption of normality is often not fulfilled, this causes the estimation of the resulting para...
Includes bibliographical references.Many important estimators in statistics have the property that t...
Not AvailableTwo new Jackknife methods, as the counterparts of two existing Bootstrap methods of var...
The technique of jackknife is applied to a general class of estimators. Considering a natural popula...
Preliminary and incomplete We propose jackknife estimators for nonlinear dynamic panel data models w...
Procedures such as Akaike information criterion (AIC), Bayesian information criterion (BIC), minimum...
<p>Jackknife tests (%) and the mean feature numbers of the SVM-based predictors for 30 samples.</p
Quenouille has developed a procedure, later termed the jackknife by Tukey, for reducing the bias of ...
We propose jackknife estimators for nonlinear dynamic panel data models with fixed effects that redu...
In this study, we adapt sufficient bootstrap into the jackknife-after-bootstrap (JaB) algorithm. The...
In this article, we describe jackknife2, a new prefix command for jackknifing linear estimators. It ...
The error or variability of machine learning algorithms is often assessed by repeatedly refitting a ...
In this paper, the delete-mj jackknife estimator is proposed. This estimator is based on samples obt...
In this paper, the delete-mj jackknife estimator is proposed. This estimator is based on samples obt...
This paper establishes estimators of the truncation point of continuous distribution using the jackk...
The assumption of normality is often not fulfilled, this causes the estimation of the resulting para...
Includes bibliographical references.Many important estimators in statistics have the property that t...
Not AvailableTwo new Jackknife methods, as the counterparts of two existing Bootstrap methods of var...
The technique of jackknife is applied to a general class of estimators. Considering a natural popula...
Preliminary and incomplete We propose jackknife estimators for nonlinear dynamic panel data models w...
Procedures such as Akaike information criterion (AIC), Bayesian information criterion (BIC), minimum...
<p>Jackknife tests (%) and the mean feature numbers of the SVM-based predictors for 30 samples.</p
Quenouille has developed a procedure, later termed the jackknife by Tukey, for reducing the bias of ...
We propose jackknife estimators for nonlinear dynamic panel data models with fixed effects that redu...
In this study, we adapt sufficient bootstrap into the jackknife-after-bootstrap (JaB) algorithm. The...