• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1155-1163
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• 2016

This paper deals with an estimation procedure of variance components in a mixed effects model by projections. Projections are used to obtain sums of squares instead of using reductions in sums of squares due to fitting both the assumed model and sub-models in the fitting constants method. A projection matrix can be obtained for the residual model at each step by a stepwise procedure to test the hypotheses. A weighted least squares method is used for the estimation of fixed effects. Satterthwaite's approximation is done for the confidence intervals for variance components.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.1
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• pp.100-106
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• 2009

This paper deals with an estimation problem of the gain and phase imbalances between the in-phase and quadrature components in the quadrature receivers which are widely used in wireless communications. It is shown that the estimates derived from the suggested auto-calibration algorithm is asymptotically minimum-variance unbiased as a function of the sampling time. In order to show this characteristic, the probability density functions of the estimates for the gain and phase imbalances are derived first. Then the mean and variance functions are investigated analytically or numerically based on the density functions.

• 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.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.595-611
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• 2016

We use nonlinear regression models (such as the Hill Model) when we analyze data in toxicology and/or pharmacology. In nonlinear regression models an estimator of parameters and estimation of measurement about uncertainty of the estimator are influenced by the variance structure of the error. Thus, estimation methods should be different depending on whether the data are homoscedastic or heteroscedastic. However, we do not know the variance structure of the error until we actually analyze the data. Therefore, developing estimation methods robust to the variance structure of the error is an important problem. In this paper we propose a method to estimate parameters in nonlinear regression models based on a preliminary test. We define an estimator which uses either the ordinary least square estimation method or the iterative weighted least square estimation method according to the results of a simple preliminary test for the equality of the error variance. The performance of the proposed estimator is compared to those of existing estimators by simulation studies. We also compare estimation methods using real data obtained from the National Toxicology program of the United States.

• Journal of Korean Society for Quality Management
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• v.10 no.1
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• pp.2-6
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• 1982

Suppose a system with m components is subjected to a random stress. We consider the estimation of reliability when data consist of random samples from the stress distribution and the strength distributions. All the distributions are assumed to be independent exponential with unknown scale parameters. An explicit form of system reliability and the minimun variance unbiased estimator are obtained. The asymptotic distribution is also obtained by expanding the minimum variance unbiased estimator about the maximum likelihood estimator and establishing their equivalance. The performance of the two estimators is compared by Monte Carlo Simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.9
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• pp.1545-1550
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• 2006

In this paper, we propose a method for applying the CLEAN algorithm to an minimum variance distortionless response(MVDR) to estimate the location of multiple targets distributed in the ocean. The CLEAN algorithm is easy to implement in a linear processor, yet not in a nonlinear processor. In the proposed method, the CSDM of a Dirty map is separated into the CSDM of a Clean beam and the CSDM of the Residual, then an individual ambiguity surface(AMS) is generated. As such, the CLEAN algorithm can be applied to an MVDR, a nonlinear processor. To solve the ill-conditioned problem related to the matrix inversiion by an MVDR when using the CLEAN algorithm, Singular value decomposition(SVD) is carried out, then the reciprocal of small eigenvalues is replaced with zero. Experimental results show that the proposed method improves the performance of an MVDR.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.251-260
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• 2009

In this study, we propose a new estimation method based on autocovariance for selecting optimal estimators of the regression coefficients in the simple linear regression model. Although this method does not seem to be intuitively attractive, these estimators are unbiased for the corresponding regression coefficients. When the exploratory variable takes the equally spaced values between 0 and 1, under mild conditions which are satisfied when errors follow an autoregressive moving average model, we show that these estimators have asymptotically the same distributions as the least squares estimators. Additionally, under the same conditions as before, we provide a self-contained proof that these estimators converge in probability to the corresponding regression coefficients.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.437-442
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• 2018

In this paper, we propose a method for estimating the position of a source in a closed form when a single source has coherently distributed property against a circular array. When a sound source reaches a sensor through multipath environments, it is seen as a distributed source and can be represented by four variables: the nominal azimuth, nominal elevation, azimuth angular spread, elevation angular spread. Therefore, it requires a lot of computation by a search method such as DSPE (Distributed Source Parameter Estimator). In this paper, we propose a method of estimating the nominal azimuth and elevation angle in a closed form using correlation function and least squares method for fast position estimation. In particular, if the source is assumed as Gaussian distribution model, the standard deviation is also estimated in a closed form. In the simulation, the validity of the proposed method is confirmed by comparing with the DSPE.

• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.94-100
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• 2001

When using a sample covariance matrix data in paucity of snapshots, adaptive matched field processing will have problem in inverting covariance matrix due to the rank deficiency. The general solutions are diagonal loading and eigenanalysis methods, but there is a significant bias in the power output. This paper presents a quantitative study of bias of power output and the performance of source localization through the simulation and the measured data analysis in fixed source case using the diagonal loading method for the minimum variance processor. Results show that the bias in power output is reduced and the performance of source localization is improved when the number of snapshots is greater than the number of array sensors.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.511-520
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• 2013

Detection outliers and robust estimators are crucial in regression models with outliers. In such studies the focus is on detecting outliers and estimating the coefficients using leave-one-out. Our study introduces Rice estimator which is an error variance estimator without estimating the coefficients. In particular, we study a comparison of the statistical properties for Rice estimator with and without outliers in simple regression models.