• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.247-257
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• 2013

A variety of practical problems can be addressed in the framework of rotation (successive) sampling. The present work presents a sample rotation pattern where sampling units are drawn on two successive occasions. The problem of estimation of population variance on current (second) occasion in two - occasion successive (rotation) sampling has been considered. A class of estimators has been proposed for population variance that includes many estimators as a particular case. Asymptotic properties of the proposed class of estimators are discussed. The proposed class of estimators is compared with the sample variance estimator when there is no matching from the previous occasion. Optimum replacement policy is discussed. Results are supported with the empirical means of comparison.

• East Asian mathematical journal
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• v.5 no.1
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• pp.95-110
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• 1989

In a general variance component model, nonnegative quadratic estimators of the components of variance are considered which are invariant with respect to mean value translaion and have minimum bias (analogously to estimation theory of mean value parameters). Here the minimum is taken over an appropriate cone of positive semidefinite matrices, after having made a reduction by invariance. Among these estimators, which always exist the one of minimum norm is characterized. This characterization is achieved by systems of necessary and sufficient condition, and by a cone restricted pseudoinverse. In models where the decomposing covariance matrices span a commutative quadratic subspace, a representation of the considered estimator is derived that requires merely to solve an ordinary convex quadratic optimization problem. As an example, we present the two way nested classification random model. An unbiased estimator is derived for the mean squared error of any unbiased or biased estimator that is expressible as a linear combination of independent sums of squares. Further, it is shown that, for the classical balanced variance component models, this estimator is the best invariant unbiased estimator, for the variance of the ANOVA estimator and for the mean squared error of the nonnegative minimum biased estimator. As an example, the balanced two way nested classification model with ramdom effects if considered.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.365-379
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• 2005

Balanced Repeated Replication(BRR) is widely used to estimate the variance of linear or nonlinear estimators from complex sampling surveys. Most of survey data sets include imputed missing values and treat the imputed values as observed data. But applying the standard BRR variance estimation formula for imputed data does not produce valid variance estimators. Shao, Chen and Chen(1998) proposed an adjusted BRR method by adjusting the imputed data to produce more accurate variance estimators. In this paper, another adjusted BRR method is proposed with examples of real data.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.154-156
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• 2017

Constant variance of degradation data over time has been generally assumed to estimate storage lifetime using destructive, accelerated degradation data over time. However, performance data of ammunitions deteriorate over time, and the standard deviation would tend to increase over time. This paper shows storage lifetime comparison results for constant variance and time-variant variance assumptions of degradation data over time, and proposes that time-variant variance assumption should be considered to increase accuracy in lifetime estimation.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.8-13
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• 2023

We propose a method for removing interference and noise to estimate target information. In wireless channels, information signals are subject to interference and noise, making it is difficult to accurately estimate the desired signal. To estimate the desired information signal, it is essential to remove the noise and interference from the received signal, extracting only the desired signal. If the received signal noise and interference are not removed, the estimated information signal will have a large error in distance and direction, and the exact location of the target cannot be estimated. This study aims to accurately estimate the desired target in space. The objective is to achieve more presice target estimation than existing methods and enhance target resolution.An estimation method is proposed to improve the accuracy of target estimation. The proposed target estimation method obtains optimal weights using linear constraints and the minimum variance method. Through simulation, the performance of the proposed method and the existing method is analyzed. The proposed method successfully estimated all four targets, while the existing method only estimated two targets. The results show that the proposed method has better resolutiopn and superior estimation capability than the existing method.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3100-3102
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• 2000

In this paper, we propose to a robust frequency offset estimation method of OFDM signals. A carrier frequency offset may be decomposed into an integer multiple of the subcarrier spacing and a residual frequency offset. Fractional part of frequency offset is obtained by using the maximum likelihood estimation(MLE) method. And we use the correlation of the samples at the output of the discrete Fourier transform(DFT) to estimate integer part of frequency offset. The result shows that the estimation frequency offset is almost linear to frequency offset. We propose to an improved estimation error variance of the carrier frequency offset estimation. The proposed estimator has better performance than the conventional ones in terms of error variance and tracking range.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.281-285
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• 2002

The observation fuzzy data in random effect and balanced designs for one way classification by using a the matrix formulation, we can estimate the fuzzy variance components for the ozone depletion example and test by the agreement index.

• 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.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.993-1002
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• 2008

Correlated responses have been widely analyzed since Liang and Zeger (1986) introduced the famous Generalized Estimating Equations(GEE). However, their variance functions were restricted to known quantifies multiplied by scale parameter. In so many industries and academic/research fields, power-of-the-mean variance function is one of the common variance function. We suggest GEE-type pseudolikelihood estimation based on the power-of-the-mean variance using existing software and investigate it's efficiency for different working correlation matrices.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.291-301
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• 2017

It is well known in a small sample that the maximum likelihood (ML) approach for variance components in the general linear model yields estimates that are biased downward. The ML estimate of residual variance tends to be downwardly biased. The underestimation of residual variance, which has implications for the estimation of marginal effects and asymptotic standard error of estimates, seems to be more serious in some limited dependent variable models, as shown by some researchers. An alternative frequentist's approach may be restricted or residual maximum likelihood (REML), which accounts for the loss in degrees of freedom and gives an unbiased estimate of residual variance. In this situation, the REML estimator is derived in a censored regression model. A small sample the REML is shown to provide proper inference on regression coefficients.