• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.953-961
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• 2006

Let's say that we are given a k number of random variables following Poisson distribution that are individually dependent and which forms multivariate Poisson distribution. We particularly dealt with a method of creating random numbers that satisfies the covariance matrix, where the elements of covariance matrix are parameters forming a multivariate Poisson distribution. To create such random numbers, we propose a new algorithm based on the method reducing the number of parameter set and deal with its relationship to the Park et al.(1996) algorithm used in creating multivariate Bernoulli random numbers.

• ETRI Journal
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• 제43권4호
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• pp.640-649
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• 2021

In this paper, an additional degree of freedom in phased multi-input multi-output (phased-MIMO) radar with any arbitrary desired covariance matrix is proposed using space-time codes. By using the proposed method, any desired transmit covariance matrix in MIMO radar (phased-MIMO radars) can be realized by employing fully correlated base waveforms such as phased-array radars and simply extending them to different time slots with predesigned phases and amplitudes. In the proposed method, the transmit covariance matrix depends on the base waveform and space-time codes. For simplicity, a base waveform can be selected arbitrarily (ie, all base waveforms can be fully correlated, similar to phased-array radars). Therefore, any desired covariance matrix can be achieved by using a very simple phased-array structure and space-time code in the transmitter. The main advantage of the proposed scheme is that it does not require diverse uncorrelated waveforms. This considerably reduces transmitter hardware and software complexity and cost. One the receiver side, multiple signals can be analyzed jointly in the time and space domains to improve the signal-to-interference-plus-noise ratio.

• Communications for Statistical Applications and Methods
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• 제21권2호
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• pp.169-181
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• 2014

Marginalized random effects models (MREM) are commonly used to analyze longitudinal categorical data when the population-averaged effects is of interest. In these models, random effects are used to explain both subject and time variations. The estimation of the random effects covariance matrix is not simple in MREM because of the high dimension and the positive definiteness. A relatively simple structure for the correlation is assumed such as a homogeneous AR(1) structure; however, it is too strong of an assumption. In consequence, the estimates of the fixed effects can be biased. To avoid this problem, we introduce one approach to explain a heterogenous random effects covariance matrix using a modified Cholesky decomposition. The approach results in parameters that can be easily modeled without concern that the resulting estimator will not be positive definite. The interpretation of the parameters is sensible. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using this method.

• 응용통계연구
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• 제17권1호
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• pp.1-12
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• 2004

본 논문에서는 2001년 경기도 양평 지역의 주민 880여명을 대상으로 반복측정되어 얻어진 혈압자료를 이용하여 반복요인이 세 개인 경우에 적절한 공분산구조를 선택하는 방법에 대해서 알아보고 혼합선형모형(mixed linear model)을 이용한 분석을 통해서 혈압에 영향을 주는 공변량(covariates)에 대해서 알아보았다.

• 지능정보연구
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• 제1권1호
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• pp.91-109
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• 1995

This paper compared four knowledge acquisition methods (namely, neural network, case-based reasoning, discriminant analysis, and covariance structure modeling) for allergic rhinitis. The data were collected from 444 patients with suspected allergic rhinitis who visited the Otorlaryngology Deduring 1991-1993. Among four knowledge acquisition methods, the discriminant model had the best overall diagnostic capability (78%) and the neural network had slightly lower rate(76%). This may be explained by the fact that neural network is essentially non-linear discriminant model. The discriminant model was also most accurate in predicting allergic rhinitis (88%). On the other hand, the CSM had the lowest overall accuracy rate (44%) perhaps due to smaller input data set. However, it was most accuate in predicting non-allergic rhinitis (82%).

• Communications for Statistical Applications and Methods
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• 제22권4호
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• pp.321-331
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• 2015

The K-means clustering algorithm is a popular and widely used method for clustering. For covariance matrices, we consider a geodesic clustering algorithm based on the K-means clustering framework in consideration of symmetric positive definite matrices as a Riemannian (non-Euclidean) manifold. This paper considers a geodesic clustering algorithm for data consisting of symmetric positive definite (SPD) matrices, utilizing the Riemannian geometric structure for SPD matrices and the idea of a K-means clustering algorithm. A K-means clustering algorithm is divided into two main steps for which we need a dissimilarity measure between two matrix data points and a way of computing centroids for observations in clusters. In order to use the Riemannian structure, we adopt the geodesic distance and the intrinsic mean for symmetric positive definite matrices. We demonstrate our proposed method through simulations as well as application to real financial data.

• Communications for Statistical Applications and Methods
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• 제31권1호
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• pp.55-64
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• 2024

In this study, we consider the sample size determination problem for clustered count data with many zeros. In general, zero-inflated Poisson and binomial models are commonly used for zero-inflated data; however, in real data the assumptions that should be satisfied when using each model might be violated. We calculate the required sample size based on a discrete Weibull regression model that can handle both underdispersed and overdispersed data types. We use the Monte Carlo simulation to compute the required sample size. With our proposed method, a unified model with a low failure risk can be used to cope with the dispersed data type and handle data with many zeros, which appear in groups or clusters sharing a common variation source. A simulation study shows that our proposed method provides accurate results, revealing that the sample size is affected by the distribution skewness, covariance structure of covariates, and amount of zeros. We apply our method to the pancreas disorder length of the stay data collected from Western Australia.

• 대한토목학회논문집
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• 제14권3호
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• pp.463-472
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• 1994

Random 지반운동에 대한 면진수조구조물 응답의 추계학적 해석방법을 연구하였다. 유연한 벽체와 내부유체간의 유체구조물 상호작용은 유체운동의 유한요소 모델링에 의하여 얻어지는 부가질량행렬 형태로 고려되었다. 정상(定常)(Stationary)지반운동으로는 수정된 Clough-Penzien Spectral Model이 사용되었으며, 비정상(非定常)(Nonstationary)지반운동으로는 상기모델에 포락함수를 적용한 모델을 사용하였다. 운동을 지배하는 Lyapunov Covariance Matrix 미분방정식의 해를 구하여 두 종류 면진시스템의 정상응답 및 비정상응답을 해석하였다.

• Journal of the Korean Data and Information Science Society
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• 제10권2호
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• pp.369-385
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• 1999

성장곡선모형에서 다중 이상값들이나 영향관측값들을 탐지하는 문제는 선형회귀모형에서의 문제에 비해 매우 복잡하여 거의 이루어지지 않고 있는 실정이다. 본 연구에서는 이상점을 포함하고 있는 성장곡선모형에서 이들을 탐지하는 방법으로 평균이동모형을 이용하는 방법을 소개하였다. 이 방법을 이용하여 찾아낸 자료가 이상점인지의 여부를 예측표본재이용 의사 베이즈 우도 기준법을 이용한 등분산성의 검정을 통해 알아보았다. 끝으로 Potthoff(1964)등이 사용한 자료를 이용한 예제를 통해 이상점 탐지와 등분 산성 검정을 실시한 결과를 제시하였다.

• Wind and Structures
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• 제18권1호
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• pp.37-56
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• 2014

The evaluation of pressure fields acting on slender structures under wind loads is currently performed in experimental aerodynamic tests. For wind-sensitive structures, in fact, the knowledge of global and local wind actions is crucial for design purpose. This paper considers a particular slender structure under wind excitation, representative of most common high-rise buildings, whose experimental wind field on in-scale model was measured in the CRIACIV boundary-layer wind tunnel (University of Florence) for several angles of attack of the wind. It is shown that an efficient reduced model to represent structural response can be obtained by coupling the classical structural modal projection with the so called blowing modes projection, obtained by decomposing the covariance or power spectral density (PSD) wind tensors. In particular, the elaboration of experimental data shows that the first few blowing modes can effectively represent the wind-field when eigenvectors of the PSD tensor are used, while a significantly larger number of blowing modes is required when the covariance wind tensor is used to decompose the wind field.