• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.235-241
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• 1996

The ordinary least squares estimator of the disturbance variance in the linear regression model with stationary errors is shown to be asymptotically unbiased when the error process has a spectral density bounded from the above and away from zero. Such error processes cover a broad class of stationary processes, including ARMA processes.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.471-477
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• 2007

In this paper, we provide a new proof to correct the asymptotic normality for the estimate $\hat{C}_{pmk}\;of\;C_{pmk}$, which is one of the well-known definitions of the process capability index. Also we comment briefly on the correction of the limiting distribution for $\hat{C}_{pmk}$ and on the use of re-sampling methods for the inference of $C_{pmk}$. Finally we discuss the concept of asymptotic unbiasedness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.5 no.7
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• pp.25-27
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• 1982

In most introductory statistics courses and text, the two equal tail test is presented without justification. In the present paper, the two equal tail critical region will be discussed in the light of unbiasedness with some test examples for the mean and the variance based on the random sample $X_1$, $X_2$,....$X_n$ from N($\mu$, $\delta^2$) using only elementary mathematics.

• Journal of the Korean Statistical Society
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• v.19 no.2
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• pp.176-181
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• 1990

Sung [1990] presented an analogue of the classical Cramer-Rao inequality for median-unbiased estimators with continuous multivariate densities depending upon a vector parameter. In the process, diffusivity, a new dispersion measure relevant to median-unbiased estimators, was defined to be a function of median-unbiased estimator's density height. In this paper we shall elaborate these ideas by defining a second kind of diffusivity and discuss the role of model-unbiasedness in median-unbiased estimation in connection with this seconde kind of diffusivity. In addition, median-unbiased estimation will be compared to mean-unbiased estimation.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.349-356
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• 2010

The ordinary least squares based estimator of the disturbance variance in a regression model for spatial panel data is shown to be asymptotically unbiased and weakly consistent in the context of SAR(1), SMA(1) and SARMA(1,1)-disturbances when there is measurement error in the regressor matrix.

• KDI Journal of Economic Policy
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• v.36 no.2
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• pp.65-101
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• 2014

This paper estimates and characterizes expected inflations using an affine term structure model based on the empirical stochastic process of the interest rates in Korea. The empirical results show that the expected inflation which marked above 4% before the global financial crisis has dampened and stabilized after the crisis. Moreover, we investigate the rationality of the various expected inflation measures in terms of the unbiasedness and efficiency and find that unbiasedness is not rejected across the all measures, while the efficiency cannot be empirically warranted. Besides, we run Granger causality tests and conclude that the expected inflations compiled from the Consensus, BOK-Expert have the cross-causality with the long-run actual inflation, while the expected inflation estimated from the term structure model has the cross-causality with the short-run actual inflation. These results connote that expected inflations collected from different sources and methods have their targets and horizons and the central bank needs to watch all of them with a balanced view instead of preferring one to the other.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1653-1660
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• 2016

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.615-624
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• 2005

In this paper, we review the variable-selection properties of LASSO and SCAD in penalized regression. To improve the weakness of SCAD for high noise level, we propose a new penalty function called MSCAD which relaxes the unbiasedness condition of SCAD. In order to compare MSCAD with LASSO and SCAD, comparative studies are performed on simulated datasets and also on a real dataset. The performances of penalized regression methods are compared in terms of relative model error and the estimates of coefficients. The results of experiments show that the performance of MSCAD is between those of LASSO and SCAD as expected.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.527-538
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• 2019

This paper considers the issue of obtaining the optimal design in Poisson regression model when the sample size is small. Poisson regression model is widely used for the analysis of count data. Asymptotic theory provides the basis for making inference on the parameters in this model. However, for small size experiments, asymptotic approximations, such as unbiasedness, may not be valid. Therefore, first, we employ the second order expansion of the bias of the maximum likelihood estimator (MLE) and derive the mean square error (MSE) of MLE to measure the quality of an estimator. We then define DM-optimality criterion, which is based on a function of the MSE. This criterion is applied to obtain locally optimal designs for small size experiments. The effect of sample size on the obtained designs are shown. We also obtain locally DM-optimal designs for some special cases of the model.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.146-148
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• 1997

An LMI-based parameterization of all $\gamma$-suboptimal reduced-order unbiased $H_{\infty}$ filters is provided in terms of a free matrix, using the unbiasedness condition, bounded real lemma and the general solution of the basic LMI. Also, by sequentially solving the generalized eigenvalue minimization and basic LMI problem, the optimal filter coefficient matrix can be obtained with the best achievable performance.