• Management Science and Financial Engineering
• /
• v.9 no.1
• /
• pp.4-10
• /
• 2003

We introduce a new estimator of the uncertainty of a jackknife estimate of standard error: the jack-knife-after-jackknife (JAJ). Using Monte Carlo simulation, we assess the accuracy of the JAJ in a variety of settings defined by statistic of interest, data distribution, and sample size. For comparison, we also assess the accuracy of the jackknife-after-bootstrap (JAB) estimate of the uncertainty of a bootstrap standard error. We conclude that the JAJ provides a useful new supplement to Tukey's jackknife, and the combination of jackknife and JAJ provides a useful alternative to the combination of bootstrap and JAB.

• Management Science and Financial Engineering
• /
• v.9 no.1
• /
• pp.1-10
• /
• 2003

We introduce a new estimator of the uncertainty of a jackknife estimate of standard error: the jack-knife-after-jackknife (JAJ). Using Monte Carlo simulation, we assess the accuracy of the JAJ in a variety of settings defined by statistic of interest, data distribution, and sample size. For comparison, we also assess the accuracy of the jackknife-after-bootstrap (JAB) estimate of the uncertainty of a bootstrap standard error. We conclude that the JAJ provides a useful new supplement to Tukey's jackknife, and the combination of jackknife and JAJ provides a useful alternative to the combination of bootstrap and JAB.

• Journal of applied mathematics & informatics
• /
• v.9 no.2
• /
• pp.869-877
• /
• 2002

In the presence of unit nonresponse we perform the calibration estimation procedure for the population total corresponding to the levels of auxiliary information and derive the Taylor and the Jackknife variance estimators of it. We study the nonresponse bias reduction and the variance stabilization, and then show the efficiency of the Taylor and the Jackknife variance estimators by simulation study.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.1
• /
• pp.193-200
• /
• 2004

Parametric estimation of truncated point in a truncated exponential distribution will be considered. The MLE, bias reducing estimator and the ordinary jackknife estimator of the truncated parameter will be compared by mean square errors. And the MME and MLE of mean parameter and estimations of the right tail probability in the distribution will be compared by their MSE's.

• Journal of the Korean Data and Information Science Society
• /
• v.6 no.1
• /
• pp.85-96
• /
• 1995

The purpose of this paper is to propose the kernel density estimator and the jackknife kernel density estimator in the presence of k's unidentified outliers, and to compare the small sample performances of the proposed estimators in a sense of mean integrated square error(MISE).

• Proceedings of the Korean Statistical Society Conference
• /
• 2002.11a
• /
• pp.327-332
• /
• 2002

Variance estimation for the regression estimator for a two-phase sample is investigated. A replication variance estimator with number of replicates equal to or slightly larger than the size of the second-phase sample is developed. In these cases, the proposed method is asymptotically equivalent to the full jackknife, but uses smaller number of replications.

• Journal of the Korean Data and Information Science Society
• /
• v.8 no.1
• /
• pp.91-97
• /
• 1997

Maximum likelihood and jackknife estimators of the location and scale parameters and right-tail probability in the truncated arcsine distribution are proposed, and we shall compare the performances of the proposed estimators in terms of bias and mean squared error.

• Journal of the Korean Data and Information Science Society
• /
• v.11 no.2
• /
• pp.303-312
• /
• 2000

We compare the MLE, UMVUE and Jackknife Estimators for the reliability function of the weibull distribution when the sample size is small.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.4
• /
• pp.1343-1348
• /
• 2006

Parametric estimation of a truncated point in a right truncated Rayleigh distribution will be considered. The MLE, a bias reduced estimator and the ordinary jackknife estimator of the truncated point in the right truncated Rayleigh distribution will be compared by mean square errors. And proposed estimators of the reliability in the right truncated Rayleigh distribution will be compared by their mean squared errors.

• Journal of the Korean Statistical Society
• /
• v.29 no.4
• /
• pp.385-394
• /
• 2000

Jackknife variance estimation based on adjusted imputed values when nonresponse is nonrandom and follow-up data are available for a subsample of nonrespondents is provided. Both hot-deck and ratio imputation method are considered as imputation method. The performance of the proposed variance estimator under nonrandom response mechanism is investigated through numerical simulation.