• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.275-285
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• 2004

We explore classification analysis using graphical methods such as sliced inverse regression and sliced average variance estimation based on dimension reduction. Some useful information about classification analysis are obtained by sliced inverse regression and sliced average variance estimation through dimension reduction. Two examples are illustrated, and classification rates by sliced inverse regression and sliced average variance estimation are compared with those by discriminant analysis and logistic regression.

• Journal of the Korean Statistical Society
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• v.35 no.1
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• pp.1-23
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• 2006

In this paper we consider an estimation of the discontinuous variance function in nonparametric heteroscedastic random design regression model. We first propose estimators of the change point in the variance function and then construct an estimator of the entire variance function. We examine the rates of convergence of these estimators and give results for their asymptotics. Numerical work reveals that using the proposed change point analysis in the variance function estimation is quite effective.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.403-410
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• 2023

Two-Phase sampling, which was first introduced by Neyman (1938), has various applications in different forms. Variance estimation for two-phase sampling has been an important research topic because conventional variance estimators used in most softwares are not working. In this paper, we considered a variance estimation for two-phase sampling in which stratified two-stage cluster sampling designs are used in both phases. By defining a conditionally unbiased estimator of an approximate variance estimator, which is calculable when all elements in the first phase sample are observed, we propose an explicit form of variance estimator of the double expanded estimator for a two-phase sample. A small simulation study shows the proposed variance estimator has a negligible bias with small variance. The suggested variance estimator is also applicable to other linear estimators of the population total or mean if appropriate residuals are defined.

• Journal of applied mathematics & informatics
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• v.8 no.3
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• pp.975-987
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• 2001

We treat the problem of variance estimation for the estimator of population total, which is derived from the calibration estimation procedure corresponding to the levels of auxiliary information under nonresponse situation. We develop the calibrated variance estimation procedure using the fact that the population total and variance as well as the sample total and variance of the auxiliary variable are known. We show that the proposed variance estimation procedure improves the $Lundst\ddot{o}rm$ and $S\ddot{a}rndal's$ (1999) procedure with respect to the variance and nonresponse bias reduction through the simulation study.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.574-576
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• 2004

Since variance of noise is important parameter for a noise filter to reduce noise in image and the performance of noise filter is dependent on estimated variance. In this paper, we apply additive noise variance estimation method to estimate variance of speckle noise of synthetic aperture radar (SAR) imagery. Generally, speckle noise is in multiplicative model, logarithmic transformation is then used to transform multiplicative model into additive model. Here, speckle noise is generally modeled as Gamma distribution function with different looks. The additive noise variance estimation is processed in log domain. The synthesis image and real image of SAR are implemented to test and confirm results and show that more accurate estimation can be achieved.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.103-108
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• 2002

We consider an estimation of discontinuous variance function in nonparametric heteroscedastic random design regression model. We first propose estimators of a change point and jump size in variance function and then construct an estimator of entire variance function. We examine the rates of convergence of these estimators and give results on their asymptotics. Numerical work reveals that the effectiveness of change point analysis in variance function estimation is quite significant.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.459-469
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• 1998

Balanced half sample method is a simple variance estimation method for complex sampling designs. Since it is simple and flexible, it has been widely used in large scale sample surveys. However, the usual BHS method overestimate the true variance in without replacement sampling and two-stage cluster sampling. Focusing on this point , we proposed an unbiased BHS variance estimator in a stratified two-stage cluster sampling and then described an implementation method of the proposed estimator. Finally, partially BHS design is explained as a tool of reducing the number of replications of the proposed estimator.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.233-238
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• 2005

S${\"{a}}$rndal (1996) and Knottnerus (2003) had a critical look at the well known variance estimator of Sen (1953) and Yates and Grundy (1953) in probability proportional to size sampling. In this paper, we point out that although their approaches can avoid the difficulties in variance estimation with respect to the joint probabilities, there exist the disadvantages in practice. Also, we describe a sampling procedure available in statistical software that are useful for the variance estimation.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.189-202
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• 1998

The goat of this paper is to study on variance estimation - Rice variance estimation, Gasser, Sroka and Jennen-Steinmetz's varince estimation - in smoothing goodness-of-fit tests. The comparisons of powers on test statistics are conducted by the change of variance, the number of oscillations, the amplitude of the alternative sample distribution.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.3
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• pp.413-422
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• 2000

In the class of models which include random effects, the variance component estimates are important to obtain accurate predictors and estimators. Variance component estimation is straightforward for balanced data but not for unbalanced data. Since orthogonality among factors is absent in unbalanced data, various methods for variance component estimation are available. REML estimation is the most widely used method in animal breeding because of its attractive statistical properties. Recently, Bayesian approach became feasible through Markov Chain Monte Carlo methods with increasingly powerful computers. Furthermore, advances in variance component estimation with complicated models such as generalized linear mixed models enabled animal breeders to analyze non-normal data.