• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.177-187
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• 1997

This article considers the power performance of the tests in unbalanced two way mixed linear models with one fixed factor. The generalized least squares (GLS) F statistic testing no differences among the effects of the levels of the fixed factor is estimated using Henderson's method III, minimum norm quadratic unbiased estimator (MINQUE) with prior guess 1, maximum likelihood (ML) and resticted maximum likelihood (REML). We investigate the power performance of these test statistics. It can be shown, through simulation, that the GLS F statistics using four estimators produce similar type I error rates and power performance.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.265-272
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• 2002

An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.103-112
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• 1994

An asymptotic property of the estimated eigenvalues for multivariate AR(p) process which consists of vector of nonstationary process and vector of stationary process is developed. All components of the nonstationary process are assumed to reveal random walk behavior. The asymptotic property is helpful in understanding multiple unit roots. In this paper we show the stationay part in multivariate AR(p) process does not affect the limiting distribution of estimated eigenvalues associated with the nonstationary process. A test statistic based on the ordinary least squares estimator for testing a certain number of multiple unit roots is suggested.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.1-10
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• 2020

We propose a unified approach to variable selection, transformation and outliers in the linear model. The procedure includes a sequential method for outlier detection and a least trimmed squares estimator for variable transformation. It uses all possible subsets regressions for model selection. Some real data analyses and the simulation results are provided to show the efficiency of the methods in the context of the correct variable selection and the fitness of the estimated model.

• Water for future
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• v.23 no.1
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• pp.79-87
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• 1990

In this study, a regression model, which relates annual flood peak flows collected at stramflow gaging stations in the Han river and Nakdong river basin to both basin characteristics and precipitation data, is developed by using the generalized least squares method which can provide reasonable and unbiased estimator of error variance by separating error variance of the regression model into that due to model error and due to sampling error. This model may be used as a mechanism for transferring hydrologic information from the gaged sites to ungaged sites.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.7
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• pp.257-266
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• 1985

A new method for the artificial beta cell which can be used to control the hyperglycemia in diabetic patients was represented. The relationship between the insulin infusion rate and the blood glucose concentration was described by the second order ARMA model, and the time varying parameters were identified by exponentially weighted least squares estimator. The design of controller was based on the pole assignment self tuning altorithm with discrete blood sampling and the constraints of input and output responsse rate were considered. The results of animal experiments show that this method may be a fruitful approach for regulating the blood glucose level. We expect that this device can be used as both therapeutic and research tools providing that its stability and reliability are improved a little more.

• 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 Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.431-440
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• 2013

We consider the problem of estimating the parameters of heavy tailed distributions. In general, maximum likelihood estimation(MLE) is the most preferred method of parameter estimation because it has good properties such as asymptotic consistency, normality and efficiency. However, MLE is not always the best solution because MLE is unstable or does not exist in some cases. This paper proposes another parameter estimation method, non-linear least squares(NLS) and compares its performance to MLE. The NLS estimator is achieved by minimizing sum of squared difference between empirical cumulative distribution function(CDF) and a theoretical distribution function. In this article, we compare the NLS method to MLE using simulated data from heavy tailed distributions. The NLS method is shown to perform better than MLE in Burr distribution when the sample size is small; in addition, it performs well in a Frechet distribution.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.123-137
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• 2018

A nonlinear mixed effects model is mainly used to analyze repeated measurement data in various fields. A nonlinear mixed effects model consists of two stages: the first-stage individual-level model considers intra-individual variation and the second-stage population model considers inter-individual variation. The individual-level model, which is the first stage of the nonlinear mixed effects model, estimates the parameters of the nonlinear regression model. It is the same as the general nonlinear regression model, and usually estimates parameters using the least squares estimation method. However, the least squares estimation method may have a problem that the estimated value of the parameters and standard errors become extremely large if the assumed nonlinear function is not explicitly revealed by the data. In this paper, a new estimation method is proposed to solve this problem by introducing the ridge regression method recently proposed in the nonlinear regression model into the first-stage individual-level model of the nonlinear mixed effects model. The performance of the proposed estimator is compared with the performance with the standard estimator through a simulation study. The proposed methodology is also illustrated using quantitative high throughput screening data obtained from the US National Toxicology Program.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9C
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• pp.720-724
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• 2008

Channel estimation in OFDM systems is usually carried out in frequency domain based on the least-squares (LS) method and the minimum mean-square error (MMSE) method with known pilot symbols. The LS estimator has a merit of low complexity but may suffer from the noise because it does not consider any noise effect in obtaining its solution. To enhance the noise immunity of the LS estimator, we consider estimation noise in time domain. Residual noise existing at the estimated channel coefficients in time domain could be reduced by reasonable selection of a threshold value. To achieve this, we propose a channel-estimation method based on a time-domain threshold which is a standard deviation of noise obtained by wavelet decomposition. Computer simulation shows that the estimation performance of the proposed method approaches to that of the known-channel case in terms of bit-error rates after the Viterbi decoder in overall SNRs.