• Proceedings of the Korean Association for Survey Research Conference
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• 2002.11a
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• pp.255-274
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• 2002

We classify rotation sampling designs into two classes. The first class replaces sample units within the same rotation group while the second class replaces sample units between different rotation groups. The first class is specified by the three-way balanced design which is a multi-level version of previous balanced designs. We introduce an extended generalized composite estimator (EGCE) and derive its variance and mean squared error for each of the two classes of design, cooperating two types of correlations and three types of biases. Unbiased estimators are derived for difference between interview time biases, between recall time biases, and between rotation group biases. Using the variance and mean squared error, since any rotation design belongs to one of the two classes and the EGCE is a most general estimator for rotation design, we evaluate the efficiency of EGCE to simple weighted estimator and the effects of levels, design gaps, and rotation patterns on variance and mean squared error.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.209-217
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• 1993

The multicollinearity problem in a multiple linear regression model may present deleterious effects on predictions. Thus, its is desirable to consider the optimal fractions with respect to the unbiased estimate of the mean squares errors of the predicted values. Interstingly, the optimal fractions can be also illuminated by the Bayesian inerpretation of the general James-Stein estimators.

• Korean Journal of Hydrosciences
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• v.6
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• pp.13-22
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• 1995

The augmenting hydrologic data using a correlation procedure has been used to improve the estimates of the mean and variance at the site of interest with short record when one or more near by sites with longer records are available. The variance of the unbiased maximum likelihood estimator of $ derived by Moran based on the multivariate normal distribytion is modified into the form of Matalas and Jacobs for the biveriate normal distribution to get the critical minimum values of the multiple correlation coefficient which give the improvement for estimating the variance at the site of interest. Those values are tabulated for various lengths of short records and the number of sites.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.147-156
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• 2002

In this paper, we study the properties of the modified winsorized mean to estimate the mean of a two-truncation parameter population. Under some mild conditions, the estimator is found to be strongly consistent and asymptotically unbiased even though the sample is doubly type II censored.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.361-373
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• 1995

Three remarks are offered, pertaining to classes of estimators Pitman-dominating a given estimator. The first remark concerns incorporating general loss in the construction of such classes. The second remark concerns Pitman domination comparisons amongst the members of such classes. The third remark concerns construction of such a class in the location-scale case.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.107-114
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• 2005

We shall consider parametric estimations for the scale parameter in the exponential distribution when the parameter is function of a known exposure level, and obtain expectations and variances for their proposed estimators. And we shall compare numerically efficiencies for proposed estimators of the scale parameter in the small sample sizes.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.421-421
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• 2004

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.205-216
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• 1996

Consider the problem of estimating a multivariate normal mean with an unknown covarience matrix under a weighted sum of squared error losses. We first provide hierarchical Bayes estimators which shrink the usual (maximum liklihood, uniformly minimum variance unbiased) estimator towards a regression surface and then prove the admissibility of these estimators using Blyth's (1951) method.

• Korean Journal of Remote Sensing
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• v.2 no.1
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• pp.3-11
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• 1986

The current methods of estimating contest distribution function in contextual clarifier are to "classify and count", GTGM (ground-truth-guided-method) and unbiased estimator. In this paper we propose a new contextual classifier echoes context distribution is replaced by context probability that is estimated from transition probability. The classification accuracy increases considerably compared with the classical one.