• Journal of the Korean Statistical Society
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• 제34권3호
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• pp.219-233
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• 2005

The 3-way balanced multi-level rotation design has been discussed (Park Kim and Kim, 2003), where the 3-way balancing is done on interview time, in monthly sample and rotation group and recall time. A greater advantage of 3-way balanced design is accomplished by an estimator. To obtain the advantage, we generalized previous generalized composite estimator (GCE). We call this as l-step GCE. The variance of the l-step GCE's of various characteristics of interest are presented. Also, we provide the coefficients which minimize the variance of the l-step GCE. Minimizing a weighted sum of variances of all concerned estimators of interest, we drive one set of the compromise coefficient of l-step GCE's to preserve additivity of estimates.

• 대한토목학회논문집
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• 제28권6B호
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• pp.723-729
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• 2008

본 연구에서는 홍수지수법의 불확실성을 평가하기 위해 우리나라 강우자료의 지역빈도해석에 적합한 것으로 제안된 generalized logistic 분포형의 quantile에 대한 점근 분산식을 이용하여 성장곡선에 대한 신뢰구간을 산정하였다. 또한 지점 빈도해석과 지역빈도해석에 의한 quantile의 분산을 이용하여 빈도해석의 효율성 지표(efficiency index)를 계산하였다. 우리나라 378개 강우 관측 지점을 바탕으로 구분한 14개 동질 지역에 대해 효율성 지표를 계산한 결과 홍수지수법이 지점빈도 해석보다 불확실성이 더 작은 quantile을 추정하는 것으로 나타났다. 한 지역에 포함되는 지점 개수가 과다하지 않도록 조정하는 것이 지역빈도해석의 효율성 측면에서 나은 것으로 나타났다.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.123-130
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• 2003

We define a broad class of rotation designs whose monthly sample is balanced in interview time, level of recall, and rotation group, and whose rotation scheme is time-invariant. The necessary and sufficient conditions are obtained for such designs. Using these conditions, we derive a minimum variance unbiased generalized composite estimator (MVUGCE). To examine the existence of time-in-sample bias and recall bias, we also propose unbiased estimators and their variances. Numerical examples investigate the impacts of design gap, non-sampling error sources, and two types of correlations on the variance of MVUGCE.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.61-70
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• 2018

Modeling of the random effects covariance matrix in generalized linear mixed models (GLMMs) is an issue in analysis of longitudinal categorical data because the covariance matrix can be high-dimensional and its estimate must satisfy positive-definiteness. To satisfy these constraints, we consider the autoregressive and moving average Cholesky decomposition (ARMACD) to model the covariance matrix. The ARMACD creates a more flexible decomposition of the covariance matrix that provides generalized autoregressive parameters, generalized moving average parameters, and innovation variances. In this paper, we analyze longitudinal count data with overdispersion using GLMMs. We propose negative binomial loglinear mixed models to analyze longitudinal count data and we also present modeling of the random effects covariance matrix using the ARMACD. Epilepsy data are analyzed using our proposed model.

• Communications for Statistical Applications and Methods
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• 제4권2호
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• pp.353-358
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• 1997

When the disturbances in the linear repression medel are generated by a seasonal autoregressive scheme the Cochrane Orcutt transformation estimator (COTE) is a well known alternative to Generalized Least Squares estimator (GLSE). In this paper it is analyzed in which situation the Ordinary Least Squares estimator (OLSE) is always better than COTE for positive autocorrelation in terms of efficiency which is here defined as the ratio of the total variances.

• 품질경영학회지
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• 제8권2호
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• pp.3-14
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• 1980

This paper is concerned with the design of an appropriate sampling plan or stopping rule and the construction of estimate for the estimation of process or lot fraction defective. Various sampling plans which are well known or have potential applications are unified into a generalized sampling plan. Under this sampling plan sufficient statistic, probability distribution, moment, and minimum variance unbiased estimate are obtained. Results for various sampling plans can be derived as special cases. Then, under given parameter values, the relative efficiencies of the various sampling plans are compared with respect to expected sample sizes and variances of estimates.

• Journal of the Korean Data and Information Science Society
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• 제26권1호
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• pp.209-216
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• 2015

Quantile regression models with covariate measurement errors have received a great deal of attention in both the theoretical and the applied statistical literature. A lot of effort has been devoted to develop effective estimation methods for such quantile regression models. In this paper we propose the partially linear support vector orthogonal quantile regression model in the presence of covariate measurement errors. We also provide a generalized approximate cross-validation method for choosing the hyperparameters and the ratios of the error variances which affect the performance of the proposed model. The proposed model is evaluated through simulations.

• Communications for Statistical Applications and Methods
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• 제16권2호
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• pp.383-388
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• 2009

In this paper we propose a variance function estimation method based on kernel trick for replicated data or data consisted of sample variances. Newton-Raphson method is used to obtain associated parameter vector. Furthermore, the generalized approximate cross validation function is introduced to select the hyper-parameters which affect the performance of the proposed variance function estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

• 전기의세계
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• 제24권5호
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• pp.72-80
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• 1975

In this paper an application of the Monte Carlo method to optimum circuit design is discussed. T. Tsuda and T. Kiyono's algorithm based on the Monte Carlo method for solving multiple simul-taneous nonlinear equations is generalized to apply it to finding solutions of the constrained nonlinear optimization problem. The generalized algorithm derived here is directly applied to economical circuit design. In the cirsuit design, the object function is a cost function which is related to the cost of each circuit component. The constraint is the variance of the total system expressed by the variances of each circuit component. The design is to be determined so that the circuit has specified drift reliability with minimum cost. A practical example of economical circuit design and a general nonlinear function minimization is presented with food results.

• Journal of the Korean Data and Information Science Society
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• 제28권4호
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• pp.927-936
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• 2017

To analyze longitudinal count data, Poisson linear mixed models are commonly used. In the models the random effects covariance matrix explains both within-subject variation and serial correlation of repeated count outcomes. When the random effects covariance matrix is assumed to be misspecified, the estimates of covariates effects can be biased. Therefore, we propose reasonable and flexible structures of the covariance matrix using autoregressive and moving average Cholesky decomposition (ARMACD). The ARMACD factors the covariance matrix into generalized autoregressive parameters (GARPs), generalized moving average parameters (GMAPs) and innovation variances (IVs). Positive IVs guarantee the positive-definiteness of the covariance matrix. In this paper, we use the ARMACD to model the random effects covariance matrix in Poisson loglinear mixed models. We analyze epileptic seizure data using our proposed model.