• 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.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.193-200
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• 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
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• v.6 no.1
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• pp.85-96
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• 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).

• 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.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 applied mathematics & informatics
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• v.9 no.2
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• pp.869-877
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• 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
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• v.17 no.3
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• pp.1021-1028
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• 2006

We shall propose ML, ordinary jackknife and biased reducing estimators of the parameter in the right truncated exponential distribution with an unidentified uniform outlier when the truncated point is unknown and their biases and MSE's are compared numerically each other in the small sample sizes.

• Journal of the Korean Mathematical Society
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• v.32 no.1
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• pp.77-84
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• 1995

Many authors have utilized an exponential distribution because of its wide applicability in reliability engineering and statistical inferences (see Bain & Engelhart(1987) and Saunders & Mann(1985)). Here we are considering the parametric estimation in an exponential distribution when its scale and location parametes are linear functions of a known exposure level t, which often occurs in the engineering and physical phenomena.

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