• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.1007-1012
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• 2003

A recursive procedure for finding the Cholesky root of the inverse of sample covariance matrix, leading to a direct solution for the inverse of a positive definite matrix, is developed using the likelihood equation for the maximum likelihood estimation of the Cholesky root under normality assumptions. An example of the Hilbert matrix is considered for an illustration of the procedure.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 추계학술대회
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• pp.705-708
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• 2010

In the paper, we have studied on algorithms for two-dimensional positioning based on GPS pseudorange Technique. First, the linearized state equation was mathematically derived based on GPS pseudorange technique. Second, the geometry model with respect to triangles formed using unit-vectors were proposed for investigation of land-based radio positioning. Finally, the corresponding mathematical formulations for DOP values and covariance matrix were designed for two-dimensional positioning.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.45-49
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• 1989

The observability of an strapdown inertial navigation system(SDINS) is investigated. The piece-wise constant systems are defined and the stepped observability matrix scheme is applied to observability analysis of SDINS theoretically, the results are compared with that of covariance simulation. It is found that SDINS is more observable than gimballed inertial navigation system (GINS) in the case of the variation of vehicle attitude, and is found that the stepped observability matrix theory is simple and useful for the analysis of the system observability but the results are not completely same as that of covariance simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.834-838
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• 1992

Tracking performance depends on the quantity of the measurement data. In the Kalman-Bucy filter and other trackers, this dependence is well understood in terms of the measurement noise covariance matrix, which specifies the uncertainty in the value of measurement inputs. In this paper, we derived approximated error covariance matrix to evaluate the dependence of target detection probability and false alarm probability in the presence of uncertainty of measurement origin.

• Journal of applied mathematics & informatics
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• 제27권5_6호
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• pp.1429-1433
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• 2009

The likelihood distance for the joint distribution of two multivariate normal distributions with common covariance matrix is explicitly derived. It is useful for identifying outliers which do not follow the joint multivariate normal distribution with common covariance matrix. The likelihood distance derived here is a good ground for the use of a generalized Wilks statistic in influence analysis of two multivariate normal data.

• 재무관리연구
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• 제12권2호
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• pp.207-219
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• 1995

This paper develops an equilibrium asset pricing model with taxation in the economy. The expected excess rate of return on a risky asset is shown to be an increasing function of the covariance of asset return with aggregate consumption rate changes and the covariance of asset return with the tax rates as well. Thus, the expected execss rate of return can be decomposed as the consumption risk premium and the tax premium. The capital asset pricing model derived in the absence of taxes is shown to understate the expected excess rate of return and to have a misspecification error in the economy with taxation.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 추계 학술발표회 논문집
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• pp.97-100
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• 2005

A new model-based clustering algorithm is proposed. The idea starts from the assumption that observations are realizations of Gaussian processes and so are correlated. With a special covariance structure, the posterior probability that an observation belongs to each cluster is computed using the ECM algorithm. A preliminary result of small-scale simulation study is given to compare with the k-means clustering algorithms.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.407-417
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• 1995

The h-plot is a graphical technique for displaying the structure of one population's variance-covariance matrix. This follows the mathematical algorithem of the principle component biplot based on the singular value decomposition. But it is known that the singular value decomposition is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, since the mathematical algorithm of the h-plot is equivalent to that of principal component biplot of Choi and Huh (1994), we derive the resistant h-plot.

• Journal of the Korean Statistical Society
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• 제27권1호
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• pp.11-23
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• 1998

A Bayes criterion for testing the equality of covariance matrices of two multivariate normal distributions is proposed and studied. Development of the criterion invloves calculation of Bayes factor using the imaginary sample method introduced by Spiegelhalter and Smith (1982). The criterion is designed to develop a Bayesian test criterion, so that it provides an alternative test criterion to those based upon asymptotic sampling theory (such as Box's M test criterion). For the constructed criterion, numerical studies demonstrate routine application and give comparisons with the traditional test criteria.

• 대한전기학회논문지
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• 제35권4호
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• pp.156-162
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• 1986

The last few years have seen a rapid development of so-called lattice algorithms for the fast solution of finite date algorithms. So far, most of the work on ladder form has been done for the prewindowed case. In this paper, the covariance lattice algorithm for instrumental variable recusions is presented. This algorithm can be used in various areas of adaptive signal processing, spectral estimation and system identification. The behavior of the proposed algorithm is illustrated by some simulation results for spectral estimation.