• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.94-100
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• 2001

When using a sample covariance matrix data in paucity of snapshots, adaptive matched field processing will have problem in inverting covariance matrix due to the rank deficiency. The general solutions are diagonal loading and eigenanalysis methods, but there is a significant bias in the power output. This paper presents a quantitative study of bias of power output and the performance of source localization through the simulation and the measured data analysis in fixed source case using the diagonal loading method for the minimum variance processor. Results show that the bias in power output is reduced and the performance of source localization is improved when the number of snapshots is greater than the number of array sensors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.169-172
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• 1999

Matched field processing technique is performed by considering complex underwater environments. Specially, tile performance of minimum variance processor is greatly degraded by eigenvalue problem. In this paper, we .propose the minimum valiance matched field processor using shaping matrix. This shaping matrix makes that the input covariance matrix is invertible and enhances the desired acoustic source component. It was proved effectively range/depth localization of the proposed method with vertical array data collected by NATO SACLANT Center north of the island of Elba off the Italian west coast.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.633-644
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• 2004

Exponentially Weighted Moving Average(EWMA) control chart for variance-covariance matrix of several quality characteristics based on accumulate-combine approach has proposed. Numerical computations show that multivariate EWMA chart based on accumulate-combine approach is more efficient than corresponding multivariate EWMA chart based on combine-accumulate approach.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1025-1038
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• 2014

Linear mixed model has often been utilized for genetic association analysis with family-based samples. The correlation matrix for family-based samples is constructed with kinship coefficient and assumes that parental phenotypes are independent and the amount of correlations between parent and offspring is same as that of correlations between siblings. However, for instance, there are positive correlations between parental heights, which indicates that the assumption for correlation matrix is often violated. The statistical validity and power are affected by the appropriateness of assumed variance covariance matrix, and in this thesis, we provide the linear mixed model with flexible variance covariance matrix. Our results show that the proposed method is usually more efficient than existing approaches, and its application to genome-wide association study of body mass index illustrates the practical value in real data analysis.

• Journal of the Korea Society for Simulation
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• v.10 no.4
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• pp.41-50
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• 2001

When optimizing a complex system by determining the optimum condition of the system parameters of interest, we often employ the process of estimating the unknown objective function, which is assumed to be a second order spline function. In doing so, we normally use common random numbers for different set of the controllable factors resulting in more accurate parameter estimation for the objective function. In this paper, we will show some mathematical result for the analysis of variance when using common random numbers in terms of the regression error, the residual error and the pure error terms. In fact, if we can realize the special structure of the covariance matrix of the error terms, we can use the result of analysis of variance for the uncorrelated experiments only by applying minor changes.

• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.111-116
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• 2015

The purpose of this study was to evaluate mixed model of dbh-height relation containing random effect. Data were obtained from a survey site for Quercus variabilis in Gangwon region and remeasured the same site after three years. The mixed model were used to fixed effect in the dbh-height relation for Quercus variabilis, with random effect representing correlation of survey period were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -0.0291, 0.1007, respectively. The model with random effect (AIC = -215.5) has low AIC value, comparison with model with fixed effect (AIC = -154.4). It is for this reason that random effect associated with categorical data is used in the data fitting process, the model can be calibrated to fit repeated site by obtaining measurements. Therefore, the results of this study could be useful method for developing model using repeated measurement.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.105-112
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• 2002

In this paper we establish bivariate exponentially weighted moving average (EWMA) control charts for autocorrelated processes using residual vectors. We first derive the residual vectors, their expectation, variance-covariance matrix, then evaluate the control chart based on the average run length (ARL).

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.127-133
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• 2001

In this article we establish multivariate cumulative sum (CUSUM) control charts based on residual vector with correlated observations. We first find the residual vector and its expectation and variance-covariance matrix and then evaluate the average run length (ARL) of the control charts.

• Journal of the Korean Statistical Society
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• v.15 no.1
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• pp.71-78
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• 1986

In this paper, multivariate discrete distribution is dealt with, where a set of r distinct counts are misreported as another set of r counts. First, the variance for the one variable marginal case is expressed in the form of an inverted parabola. Next, for the multivariate negative binomial case, elements of the covariance matrix are evaluated with reference to asymptotic distributions. Finally, for the same case of multivariate negative binomial, Bayesian estimates of the parameters and of the modification rates are provided.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.333-342
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• 2008

Variable selection theorem in the linear regression model is extended to the analysis of covariance model. When some of regression variables are omitted from the model, it reduces the variance of the estimators but introduces bias. Thus an appropriate balance between a biased model and one with large variances is recommended.