• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.211-219
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• 2015

Generalized linear mixed models are used to analyze longitudinal categorical data. Random effects specify the serial dependence of repeated outcomes in these models; however, the estimation of a random effects covariance matrix is challenging because of many parameters in the matrix and the estimated covariance matrix should satisfy positive definiteness. Several approaches to model the random effects covariance matrix are proposed to overcome these restrictions: modified Cholesky decomposition, moving average Cholesky decomposition, and partial autocorrelation approaches. We review several approaches and present potential future work.

• ETRI Journal
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• v.43 no.4
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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.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.42-51
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• 2020

In this paper, the authors propose the pseudo complex correlation coefficient (PCCC) of the two complex random variables (RV), because the four real correlation coefficients (RCC) of the corresponding four real RVs cannot be obtained only from the complex correlation coefficient (CCC) of given two complex RV. Such observation is motivated by the general statement; "The complex jointly-Gaussian random M-vector cannot be completely described by the complex covariance matrix, even though the real Gaussian random 2M-vector can be completely descried by the real covariance matrix. Therefore, in order to describe completely the complex jointly-Gaussian random M-vector, we need an additional matrix, namely the complex pseudo-covariance matrix, along with the complex covariance matrix." Then, we apply PCCC to correlated information sources (CIS) for non-orthogonal multiple access (NOMA) in 5G system, and investigate impact of the proposed PCCC on the achievable data rate of the stronger channel user in the conventional successive interference cancellation (SIC) NOMA with CIS. It is shown that for the given same CCC, the achievable data rates with the different PCCC are different, because the corresponding RCC are different. We also show that as the absolute value of the same CCC increases, the impact of the different PCCC becomes more significant.

• Water for future
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• v.29 no.5
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• pp.215-222
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• 1996

An expression for the cross covariance of the logconductivity and the head in nonstationary porous formation is obtained. This cross covariance plays a key role in the inverse problem, i.e., in inferring the statistical characteristics of the conductivity field from head data. The nonstationary logconductivity is modeled as superposition of definite linear trend and stationary fluctuation and the hydraulic head in saturated aquifers is found through stochastic analysis of a steady, two-dimensional flow. The cross covariance with a Gaussian correlation function is investigated for two particular cases where the trend is either parallel or normal to the head gradient. The results show that cross covariances are stationary except along separation distances parallel to the mean flow direction for the case where the trend is parallel to head gradient. Also, unlike the stationary model, the cross covariance along distances normal to flow direction is non-zero. From these observations we conclude that when a trend in the conductivity field is suspected, this information must be incorporated in the analysis of groundwater flow and solute transjport.

• Ocean and Polar Research
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• v.26 no.3
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• pp.453-463
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• 2004

The Antarctic Peninsula is important in terms of global warming research due to pronounced increase of air temperature over the last century. The first eddy covariance system was established at King Sejong Station located in the northern region of the Antarctic Peninsula in December of 2002 and has been operated over one year. Here, we analyze turbulent characteristics to determine quality control criteria for turbulent sensible heat flux data as well as to diagnose the possibility of long term eddy covariance measurement under extreme weather conditions of the Antarctic Peninsula. We also report the preliminary result on sensible heat flux. Based on the analyses on turbulent characteristics such as integral turbulence characteristics of vertical velocity (w) and heat (T), stationarity test and investigation of correlation coefficient, they fallow the Monin-Obukhov similarity and eddy covariance flux data were reliable. ${\sim}47%$ of total retrieved sensible heat flux data could be used for further analysis. Daytime averaged sensible heat flux showed a pronounced seasonal variation, with a maximum of up to $300Wm^{-2}$ in summer. In conclusion, continuous and long-term eddy covariance measurement may be possible at the study site and the land surface may influence the atmosphere significantly through heat transport in summer.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.1
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• pp.15-23
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• 2003

This paper proposes a fast direction of arrival (DOA) estimation algorithm that can rapidly estimate incidence angles of incoming signals using a pseudo covariance matrix. The conventional subspace DOA estimation methods such as MUSIC (multiple signal classification) algorithms need many sample signals to acquire covariance matrix of input signals. Thus, it is difficult to estimate the DOAs of signals because they cannot perform DOA estimation during receiving sample signals. Also if the D0As of signals are changing rapidly, conventional algorithms cannot estimate incidence angles of signals exactly. The proposed algorithm obtains bearing response and directional spectrum after acquiring pseudo covariance matrix of each snapshot. The incidence angles can be exactly estimated by using the bearing response and directional spectrum. The proposed DOA estimation algorithm uses only concurrent snapshot so as to obtain covariance matrix. Compared to conventional DOA estimation methods. The proposed algorithm has an advantage that can estimate DOA of signal rapidly.

• Ocean and Polar Research
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• v.30 no.2
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

• The korean journal of orthodontics
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• v.53 no.3
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• pp.185-193
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• 2023

Objective: The growth and development of the mandible strongly depend on modeling changes occurring at its ramus. Here, we investigated covariance patterns between the morphology of the ramus and the rest of the face. Methods: Lateral cephalograms of 159 adults (55 males and 104 females) with no history of orthodontic treatment were collected. Geometric morphometrics with sliding semi-landmarks was used. The covariance between the ramus and face was investigated using a two-block partial least squares analysis (PLS). Sexual dimorphism and allometry were also assessed. Results: Differences in the divergence of the face and anteroposterior relationship of the jaws accounted for 24.1% and 21.6% of shape variation in the sample, respectively. Shape variation was greater in the sagittal plane for males than for females (30.7% vs. 17.4%), whereas variation in the vertical plane was similar for both sexes (23.7% for males and 25.4% for females). Size-related allometric differences between the sexes accounted for the shape variation to a maximum of 6% regarding the face. Regarding the covariation between the shapes of the ramus and the rest of the face, wider and shorter rami were associated with a decreased lower anterior facial height as well as a prognathic mandible and maxilla (PLS 1, 45.5% of the covariance). Additionally, a more posteriorly inclined ramus in the lower region was correlated with a Class II pattern and flat mandibular plane. Conclusions: The width, height, and inclination of the ramus were correlated with facial shape changes in the vertical and sagittal planes.

• Annals of Clinical Neurophysiology
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• v.25 no.2
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• pp.93-102
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• 2023

Background: We aimed to identify any differences in the structural covariance network based on structural volume and those in the functional network based on cerebral blood flow between the ipsilateral and contralateral hemispheres of pain in patients with episodic migraine without aura. Methods: We prospectively enrolled 27 patients with migraine without aura, all of whom had unilateral migraine pain. We defined the ipsilateral hemisphere as the side of migraine pain. We measured structural volumes on three-dimensional T1-weighted images and cerebral blood flow using arterial spin labeling magnetic resonance imaging. We then analyzed the structural covariance network based on structural volume and the functional network based on cerebral blood flow using graph theory. Results: There were no significant differences in structural volume or cerebral blood flow between the ipsilateral and contralateral hemispheres. However, there were significant differences between the hemispheres in the structural covariance network and the functional network. In the structural covariance network, the betweenness centrality of the thalamus was lower in the ipsilateral hemisphere than in the contralateral hemisphere. In the functional network, the betweenness centrality of the anterior cingulate and paracingulate gyrus was lower in the ipsilateral hemisphere than in the contralateral hemisphere, while that of the opercular part of the inferior frontal gyrus was higher in the former hemisphere. Conclusions: The present findings indicate that there are significant differences in the structural covariance network and the functional network between the ipsilateral and contralateral hemispheres of pain in patients with episodic migraine without aura.

• Journal of the Korean Statistical Society
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• v.20 no.2
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• pp.191-201
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• 1991

Given a set of data pxN$_{i}$, matrices X$_{i}$ observed from p-variate normal populations $\prod$$_{i}$~N($\mu$$_{I}$, $\Sigma$$_{i}$) for i=1, …, K, the test for equality form of the covariance matrices is to choose a hypothetical model which best explains the homogeneity/heterogeneity structure across the covariance matrices among the hypothesized class of models. This paper describes a test procedure for selecting the best model. The procedure is based on a synthesis of Bayesian and a cross-validation or sample reuse methodology that makes use of a one-at-a-time schema of observational omissions. Advantages of the test are argued on two grounds, and illustrative examples and simulation results are given.are given.