• Management Science and Financial Engineering
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• v.9 no.1
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• pp.4-10
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• 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
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• v.9 no.1
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• pp.1-10
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• 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 the Korean Data and Information Science Society
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• v.8 no.1
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• pp.59-70
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• 1997

We introduce several estimators of the location and the scale parameters of the two-parameter exponential distribution, and then compare these estimators by the mean square error (MSE). Using the parametric bootstrap estimators and the delete-d jackknife, we obtain the bootstrap and the delete-d jackknife confidence intervals for the location and the scale parameters and compare the bootstrap confidence intervals with the delete-d jackknife confidence intervals by length and coverage probability through Monte Carlo method.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.111-119
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• 2002

One of the advantages of SP methods is the possibility of getting a number of responses from each respondent. However, when the repeated observations from each respondent are analysed by applying the simple modeling method, a potential problem is created because of upbiased significance due to the repeated observation from each respondent. This study uses a variety of approaches to explore this issue and to test the robustness of the simple model estimates. Among several different approaches, the Jackknife method and Kocurs method were applied. The Jackknife method was implemented using a program JACKKNIFE. The model estimate results of Jackknife method and Kocurs method were compared with those of the uncorrected estimates in order to test whether there was repeated measurement problem or not and the extent to which this problem affected the model estimates. The standard errors between the uncorrected model estimates and Jackknife estimates were also compared. The results reveals that the t-ratios of Kocurs are much lower than those of the uncorrected method and Jackknife estimates, indicating that Kocurs method underestimates the significance of the coefficients. Jackknife method produced the almost same coefficients as those of the uncorrected model but the lower t-ratios. These results indicate that the coefficients of the uncorrected method are accurate but that their significance are somewhat overestimated. In this study. 1 concluded that the repeated measurement Problem did exist in our data, but that it did not affect the model estimation results significantly. It is recommended that such a test should become a standard procedure. If it turns out that the analysis based on the simple uncorrected method are influenced by the repeated measurement problem. it should be corrected.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.487-492
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• 2006

Jackknife estimators for parameters in the left truncated exponential model are presented. And we show that the generalized jackknife estimators are more efficient than others in terms of the bias and the mean squared error.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.869-878
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• 1998

In this paper, we propose some jackknife estimators for mean in the exponential model with grouped and censored data. Also, we compare the proposed jackknife estimators to other approximate estimators in terms of the mean square error and bias.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.91-97
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• 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
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• v.11 no.2
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• pp.303-312
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• 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
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• v.5 no.1
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• pp.21-32
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• 1994

Effects on the scale and location parameters in the exponential model when both parameters changes will be considered, based on the complete or truncated samples by the maximum likelihood and jackknife methods.

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