• Journal of the Korean Statistical Society
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• 제25권2호
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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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• 제36권4호
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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.

• 산업경영시스템학회지
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• 제5권7호
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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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• 제19권2호
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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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• 제17권3호
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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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• 제36권2호
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• pp.65-101
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• 2014

본 연구에서는 이자율 변수에 적절한 확률과정을 부여하고 이를 가격함수에 직접 대입한 뒤 최종적으로 자산가격 PDE를 도출하는 재무모형을 이용하여 우리나라 기대인플레이션을 추정하고 그 특성을 살펴보고자 한다. 우리나라의 기대인플레이션은 글로벌 금융위기 이전에는 4%를 상회하는 높은 수준을 보였으나 2008년 후반을 기점으로 하향 안정화되는 모습을 시현했다. 또한 수익률곡선에서 도출된 기대인플레이션과 서베이 등을 통해 실제로 자료의 입수가 가능한 기대인플레이션을 이용하여 경제주체의 기대인플레이션 형성에 체계적인 편의가 존재하지 않고(불편성), 기대형성에 활용 가능한 모든 정보가 반영되었는지(효율성)를 기준으로 합리성을 평가한 결과, 분석에 이용된 모든 기대인플레이션에서 효율성은 기각되나 기대편의는 발생하지 않는 것으로 나타났다. 한편, 실제인플레이션과 기대인플레이션 간의 Granger 인과관계 검정 결과, 대체로 컨센서스 및 BOK 전문가 기대인플레이션은 상대적으로 장기의 실제인플레이션과, 이자율모형에서 도출된 기대인플레이션은 단기의 실제인플레이션과 상호 인과관계가 발생하는 것으로 분석되었다. 이러한 결과는 각각의 인플레이션 지표들이 내포하는 인플레이션의 정보가 상이하다는 것을 방증하고 있다. 이는 중앙은행이 하나의 인플레이션 지표보다는 다양한 지표들을 균형 있는 시각으로 관찰하는 것이 필요함을 시사한다.

• Journal of the Korean Data and Information Science Society
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• 제27권6호
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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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• 제12권3호
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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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• 제26권6호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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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.