Quantile function plays an important role in statistical inference, and intermediate quantile is useful in risk management. It is known that jackknife method fails for estimating the variance of a sample quantile. By assuming that the underlying distribution satisfies some extreme value conditions, we show that Jackknife variance estimator is inconsistent for an intermediate order statistic. Further we derive the asymptotic limit of the Jackknife-Studentized intermediate order statistic so that a confidence interval for an intermediate quantile can be obtained. A Simulation study is conducted to compare this new confidence interval with other existing ones in terms of coverage accuracy. (C) 2008 Elsevier B.V. All rights reserved.Statistics ...
In this paper we consider the problem of frequentist model averaging for quantile regression (QR) wh...
This study is a continuing investigation into the performance of confidence intervals for the differ...
Published in Journal of Econometrics https://doi.org/10.1016/j.jeconom.2014.11.005</p
<p>The jackknife estimation of variance for the median, using the original measurement scale, has be...
Variance of the sample median from discrete distributions is estimated by the bootstrap and by the j...
We show that that the jackknife variance estimator vjack and the the infinitesimal jackknife varianc...
This paper presents a method for constructing confidence intervals using the jackknife. Asymptotic e...
Quenouille has developed a procedure, later termed the jackknife by Tukey, for reducing the bias of ...
Se considera la estimación de cuantiles en poblaciones finitas mediante la técnica jackknife. Se emp...
This paper explores the properties of jackknife methods of estimation in stationary autoregressive m...
Not AvailableTwo new Jackknife methods, as the counterparts of two existing Bootstrap methods of var...
We show that the coverage error of confidence intervals and level error of hypothesis tests for popu...
Includes bibliographical references.Many important estimators in statistics have the property that t...
In statistics it is of interest to find a better interval estimator of the absolute mean deviation. ...
Existing jackknife variance estimators used with sample surveys can seriously overestimate the true ...
In this paper we consider the problem of frequentist model averaging for quantile regression (QR) wh...
This study is a continuing investigation into the performance of confidence intervals for the differ...
Published in Journal of Econometrics https://doi.org/10.1016/j.jeconom.2014.11.005</p
<p>The jackknife estimation of variance for the median, using the original measurement scale, has be...
Variance of the sample median from discrete distributions is estimated by the bootstrap and by the j...
We show that that the jackknife variance estimator vjack and the the infinitesimal jackknife varianc...
This paper presents a method for constructing confidence intervals using the jackknife. Asymptotic e...
Quenouille has developed a procedure, later termed the jackknife by Tukey, for reducing the bias of ...
Se considera la estimación de cuantiles en poblaciones finitas mediante la técnica jackknife. Se emp...
This paper explores the properties of jackknife methods of estimation in stationary autoregressive m...
Not AvailableTwo new Jackknife methods, as the counterparts of two existing Bootstrap methods of var...
We show that the coverage error of confidence intervals and level error of hypothesis tests for popu...
Includes bibliographical references.Many important estimators in statistics have the property that t...
In statistics it is of interest to find a better interval estimator of the absolute mean deviation. ...
Existing jackknife variance estimators used with sample surveys can seriously overestimate the true ...
In this paper we consider the problem of frequentist model averaging for quantile regression (QR) wh...
This study is a continuing investigation into the performance of confidence intervals for the differ...
Published in Journal of Econometrics https://doi.org/10.1016/j.jeconom.2014.11.005</p