• 응용통계연구
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• 제21권5호
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• pp.783-789
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• 2008

We investigate the effects of the misspecification of cointegrating(CI) ranks at other frequencies on the inference of seasonal models at the frequency of interest; our study includes tests for CI ranks and estimation of CI vectors. Earlier studies focused mostly on a single frequency corresponding to one seasonal root at a time, ignoring possible cointegration at the remaining frequencies. We investigate the effects of the mis-specification, especially in finite samples, by adopting Gaussian reduced rank(GRR) estimation by Ahn and Reinsel (1994) that considers cointegration at all frequencies of seasonal unit roots simultaneously. It is observed that the identification of the seasonal CI rank at the frequency of interest is sensitive to the mis-prespecification of the CI ranks at other frequencies, mainly when the CI ranks at the remaining frequencies are underspecified.

• Communications for Statistical Applications and Methods
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• 제15권5호
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• pp.719-726
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• 2008

In this paper we consider a joint test for seasonal cointegrating(CI) ranks that enables us to simultaneously model cointegrated structures across seasonal unit roots in seasonal cointegration. A CI rank test for a single seasonal unit root is constructed and extended to a joint test for multiple seasonal unit roots. Their asymptotic distributions and selected critical values for the joint test are obtained. Through a small Monte Carlo simulation study, we evaluate performances of the tests.

• Communications for Statistical Applications and Methods
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• 제22권1호
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.615-624
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• 2015

We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.

• 전자공학회논문지B
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• 제29B권2호
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• pp.18-26
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• 1992

MR(Model Reduction) is presented in order to estimate the frequency of multiple sinusoids from the finite noisy data with the white or colored noises. MR, using the reduced rank models, is designed, appling the approximation of linear system to LP(Linear Prediction). The MR method is analyzed. Monte-carlo simulations are conducted for MR and Lp. The results are compared with in terms of mean, root-mean square and relative bias. MR eliminates effectevely the extremeous and exceptional poles appearing in LP and improves the accuracy of LP. Especially, MR gives promising results in short noisy measurements, low SNR's and colored noises. Power spectral density and angular frequency position are showed by figures, for examples. Finally, the new method is utilized to the communication and biomedical systems estimating the characteristics of the signal and the system identification modelling the dynamic systems from experimental data.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1993년도 학술논문발표회 논문집 제12권 1호
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• pp.75-79
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• 1993

본 논문에서는 변화가 심한 음원파형을 추정하는 새로운 순차처리 알고리듬을 제안한다. 먼저, 1) 기존의 순차처리 분석법중 대표적인 분석법인 RLS(recursive least square)의 문제점들을 검토하고, 2) 이를 개선하기 위해서 관측행렬(observation matrix)을 최적차수의 SVD(reduced-rank singular value decomposition)로 재구성하고, 3) 이에 견실개념(robustness concept)을 적용해서 최적의 성도변수(vocal tract parameter)를 찾아내고 역필터를 적용해서 음원(voice source)을 효과적으로 구분해낸다. 본 논문에서 제안된 방법으로 음원을 추정할 경우, 변화가 심한 음원파형을 잘 추정할 수 있으며, 음원의 특성을 구분해낸 성도 파라미터도 효과적으로 추정할 수 있다. 본 연구내용은 음성합성에서 자연성 개선 및 개인성 구현을 위해서 필수적이며, 다양한 형태의 음성을 표현하기 위해 사용되어질 수 있다. 또한, 음성코딩, 화자인식, 음성인식에서도 사용되어질 수 있다.

• Communications for Statistical Applications and Methods
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• 제15권3호
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• pp.411-420
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• 2008

This paper considers a feasible two-step estimator for seasonal cointegration as the extension of $Br{\ddot{u}}ggeman$ and $L{\ddot{u}}tkepohl$ (2005). It is shown that the reducedrank maximum likelihood(ML) estimator for seasonal cointegration can still produce occasional outliers as that for non-seasonal cointegration even though the sizes of them are not extreme as those in non-seasonal cointegration. The ML estimator(MLE) is compared with the two-step estimator in a small Monte Carlo simulation study and we find that the two-step estimator can be an attractive alternative to the MLE, especially, in a small sample.

• 문화경제연구
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• 제20권2호
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• pp.3-18
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• 2017

본 연구에서는 콘텐츠산업 인력의 업무 자율성 변화를 분석했다. 한국근로환경조사(2011, 2014) 자료에서 콘텐츠산업 인력과 업무 자율성 변수를 추출하고 이중차감추정법을 활용하여 분석한 결과 전체 표본에서는 콘텐츠산업 인력의 업무 자율성(업무 순서, 업무 방법, 업무속도)에 유의미한 변화가 나타나지 않았다. 반면 최근 콘텐츠업계의 자율성 감소는 논의는 대규모 인력과 자본이 투입되는 경우라는 것을 고려하여 30인 이상 사업체 표본을 대상으로 분석하면 콘텐츠산업 인력의 자율성이 유의미하게 감소한 것으로 나타난다. 업무의 자율성은 직위와 역량과는 정(+)의 관계가, 사업체 규모와는 부(-)의 관계가 나타난다. 본 연구는 그동안 사례 중심으로 논의되어 왔던 콘텐츠산업 인력의 업무 자율성 변화를 실증분석했다는 의의를 가진다. 또한, 인력들의 부여되는 자율성이 콘텐츠 제작 유통 과정에서 긍정적인 효과를 창출하기 위해서는 인력에 대한 보상 및 관리 체계 개선이 수반되어야 함을 제시하였다.