• Journal of applied mathematics & informatics
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• 제7권2호
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• pp.653-664
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• 2000

The problem of inference about the unknown change-point with a change in mean is considered. We suggest a nonparametric change-point estimator using window and prove its consistency when the errors are from the distribution with the mean zero and the common variance. a comparison study is done by simulation on the mean, the variance, and the proportion of matching the true change-points.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.118-128
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• 2017

Uncertain factors in finical markets make the prediction of future returns and risk of asset much difficult. In this paper, a model,assuming the admissible errors on expected returns and risks of assets, assisted in the multiperiod mean variance portfolio selection problem is built. The model considers transaction costs, upper bound on borrowing risk-free asset constraints, cardinality constraints and threshold constraints. Cardinality constraints limit the number of assets to be held in an efficient portfolio. At the same time, threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Because of these limitations, the proposed model is a mix integer dynamic optimization problem with path dependence. The forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, to evaluate the model, our result of a meaning example is compared to the terminal wealth under different constraints.

• 품질경영학회지
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• 제30권1호
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• pp.118-132
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• 2002

This paper presents a process incapability index to provide manager with various information of process and to reduce cost. The introduced process incapability indices indicate information about mean and variance of manufacturing process and variance of inspection process to evaluate process capability using ratio of variance and difference between target and mean to specification. This model can be used by the scale of six sigma management.

• Communications for Statistical Applications and Methods
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• 제4권1호
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• pp.177-183
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• 1997

A data-adaptive order selection procedure is proposed for local polynomial nonparametric regression. For each given polynomial order, bias and variance are estimated and the adaptive polynomial order that has the smallest estimated mean squared error is selected locally at each location point. To estimate mean squared error, empirical bias estimate of Ruppert (1995) and local polynomial variance estimate of Ruppert, Wand, Wand, Holst and Hossjer (1995) are used. Since the proposed method does not require fitting polynomial model of order higher than the model order, it is simpler than the order selection method proposed by Fan and Gijbels (1995b).

• 한국경영과학회지
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• 제39권4호
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• pp.137-152
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• 2014

In this paper, I suggest several techniques to estimate covariance matrix and compare the performance of the global minimum variance portfolio (GMVP) in terms of out of sample mean standard deviation and return. As a result, the return differences among the GMVPs are insignificant. The mean standard deviation of the GMVP using historical covariance is sensitive to the estimation window and the number of assets in the portfolio. Among the model covariance, the GMVP using constant systematic risk ratio model or using short sale restriction shows the best performance. The performance difference between the GMVPs using historical covariance and model covariance becomes insignificant as the historical covariance is estimated with longer estimation window. Lastly, the implied volatilities from ELW prices do not lead to superior performance to the historical variance.

• Journal of the Korean Data and Information Science Society
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• 제22권3호
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• pp.381-387
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• 2011

본 논문은 일원 확률모형의 가정하에 실험자료를 분석할 때 확률모형과 관련된 분산성분을 추정하는 문제를 다루고 있다. 분산성분의 추정방법으로 적률법을 이용하고 있다. 적률법을 이용할 때 필요한 두 가지 계산과정은 요인의 변동에 따른 제곱합과 제곱합의 기대값 계산이다. 제곱합의 계산으로 사영을 어떻게 이용하는 가를 논의하고 있다. 제곱합의 기대값 계산을 위해 분산성분의 계수로 관측되는 관련행렬의 고유근을 이용하는 방법을 다루고 있다. 분산성분의 적률추정량으로 사영과 고유근을 이용한 분산성분의 추정방법이 Hartley (1967)의 합성법보다 간편하고 효율적인 방법임을 논의하고 있다.

• Communications for Statistical Applications and Methods
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• 제15권6호
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• pp.993-1002
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• 2008

Correlated responses have been widely analyzed since Liang and Zeger (1986) introduced the famous Generalized Estimating Equations(GEE). However, their variance functions were restricted to known quantifies multiplied by scale parameter. In so many industries and academic/research fields, power-of-the-mean variance function is one of the common variance function. We suggest GEE-type pseudolikelihood estimation based on the power-of-the-mean variance using existing software and investigate it's efficiency for different working correlation matrices.

• Journal of the Korean Data and Information Science Society
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• 제20권3호
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• pp.577-585
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• 2009

So far in a nonparametric regression model one of the interesting problems is estimating the error variance. In this paper we propose an estimator of the mean of the squared functions which is the numerator of SNR (Signal to Noise Ratio). To estimate SNR, the mean of the squared function should be firstly estimated. Our focus is on estimating the amplitude, that is the mean of the squared functions, in a nonparametric regression using a simple linear regression model with the quadratic form of observations as the dependent variable and the function of a lag as the regressor. Our method can be extended to nonparametric regression models with multivariate functions on unequally spaced design points or clustered designed points.

• 말소리와 음성과학
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• 제12권3호
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• pp.65-71
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• 2020

특징 정규화는 음성 특징 파라미터들의 통계적인 특성의 정규화를 통해 훈련 및 테스트 조건 사이의 환경 불일치의 영향을 감소시키는 방법으로서 기존의 Gaussian mixture model-hidden Markov model(GMM-HMM) 기반의 음성인식 시스템에서 우수한 성능개선을 입증한 바 있다. 하지만 심층신경망(deep neural network, DNN) 기반의 음성인식 시스템에서는 환경 불일치의 영향을 최소화 하는 것이 반드시 최고의 성능 개선으로 연결되지는 않는다. 본 논문에서는 이러한 현상의 원인을 과도한 특징 정규화로 인한 정보손실 때문이라 보고, 음향모델을 훈련 하는데 유용한 정보는 보존하면서 환경 불일치의 영향은 적절히 감소시켜 음성인식 성능을 최대화 하는 특징 정규화 방식이 있는 지 검토해보고자 한다. 이를 위해 평균 정규화(mean normalization, MN)와 평균 및 분산 정규화(mean and variance normalization, MVN)의 절충 방식인 평균 및 지수적 분산 정규화(mean and exponentiated variance normalization, MEVN)를 도입하여, 잡음 및 잔향 환경에서 분산에 대한 정규화의 정도에 따른 DNN 기반의 음성인식 시스템의 성능을 비교한다. 실험 결과, 성능 개선의 폭이 크지는 않으나 분산 정규화의 정도에 따라 MEVN이 MN과 MVN보다 성능이 우수함을 보여준다.

• Journal of the Korean Statistical Society
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• 제19권2호
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• pp.139-144
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• 1990

For the first-order bilinear time series model $X_t = aX_{t-1} + e_i + be_{t-1}X_{t-1}$ where ${e_i}$ is a sequence of independent normal random variables with mean 0 and variance $\sigma^2$, the asymptotic distribution of sample autocarrelation function is obtained and shown to follow a normal distribution. The variance of the asymptotic distribution is of a complicated form and hence a bootstrap estimate of the variance is proposed for large sample inference. This result can be used to distinguish between different bilinear models.