• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.65-83
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• 2022

Although various statistical methods have been developed to map time-dependent genetic factors, most identified genetic variants can explain only a small portion of the estimated genetic variation in longitudinal traits. Gene-gene and gene-time/environment interactions are known to be important putative sources of the missing heritability. However, mapping epistatic gene-gene interactions is extremely difficult due to the very large parameter spaces for models containing such interactions. In this paper, we develop a Gaussian process (GP) based nonparametric Bayesian variable selection method for longitudinal data. It maps multiple genetic markers without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the GP model measures the importance of each marker, regardless of whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. To improve the flexibility of the GP model, we propose a novel grid-based method for the within-subject dependence structure. The proposed method can accurately approximate complex covariance structures. The dimension of the covariance matrix depends only on the number of fixed grid points although each subject may have different numbers of measurements at different time points. The deviance information criterion (DIC) and the Bayesian predictive information criterion (BPIC) are proposed for selecting an optimal number of grid points. To efficiently draw posterior samples, we combine a hybrid Monte Carlo method with a partially collapsed Gibbs (PCG) sampler. We apply the proposed GP model to a mouse dataset on age-related body weight.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• 제22권1호
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• pp.89-97
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• 2011

We analyze the performance of various techniques to overcome degradation of performance of STAP caused by nonhomogeneous clutter. The performance of NHD that used to eliminate outliers from nonhomogeneous clutter is improved by using the projection statistics(PS) that is robust to multiple outliers. The method of clutter covariance matrix estimation using a median value and the conventional method are also investigated and then compared. From the simulation results of STAP, the method of clutter covariance matrix estimation using a median value shows better performance than the conventional method for the calculation of the SINR loss, and MSMI for the single target and the multiple targets regardless of the NHD methods.

• Journal of Positioning, Navigation, and Timing
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• 제12권3호
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• pp.271-280
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• 2023

Pedestrian Dead Reckoning (PDR) methods using initial sensors are being studied to provide the location information of smart device users in indoor environments where satellite signals are not available. PDR can continuously estimate the location of a pedestrian regardless of the walking environment, but has the disadvantage of accumulating errors over time. Unlike this, WiFi signal-based wireless positioning technology does not accumulate errors over time, but can provide positioning information only where infrastructure is installed. It also shows different positioning performance depending on the environment. In this paper, an integrated positioning technology integrating two positioning techniques with different error characteristics is proposed. A technique for correcting the error of PDR was designed by using the location information obtained through WiFi Measurement-based fingerprinting as the measurement of Extended Kalman Filte (EKF). Here, a technique is used to variably calculate the error covariance of the filter measurements using the WiFi Fingerprinting DB and apply it to the filter. The performance of the proposed positioning technology is verified through an experiment. The error characteristics of the PDR and WiFi Fingerprinting techniques are analyzed through the experimental results. In addition, it is confirmed that the PDR error is effectively compensated by adaptively utilizing the WiFi signal to the environment through the EKF to which the adaptive error covariance proposed in this paper is applied.

• Journal of the Korean Data Analysis Society
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• 제20권6호
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• pp.2733-2746
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• 2018

The two inverse regression estimation methods, SIR and SAVE to estimate the central space are computationally easy and are widely used. However, SIR and SAVE may have poor performance in finite samples and need strong assumptions (linearity and/or constant covariance conditions) on predictors. The two non-parametric estimation methods, MAVE and dMAVE have much better performance for finite samples than SIR and SAVE. MAVE and dMAVE need no strong requirements on predictors or on the response variable. MAVE is focused on estimating the central mean subspace, but dMAVE is to estimate the central space. This paper explores and compares four methods to explain the dimension reduction. Each algorithm of these four methods is reviewed. Empirical study for simulated data shows that MAVE and dMAVE has relatively better performance than SIR and SAVE, regardless of not only different models but also different distributional assumptions of predictors. However, real data example with the binary response demonstrates that SAVE is better than other methods.

• Journal of Environmental Science International
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• 제19권12호
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• pp.1375-1384
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• 2010

The purpose of this research is to understand the change of runoff characteristics by estimated spatial rainfall. Therefore, this paper largely composed of two parts. First, we compared the simulated result according to estimation method, ID(Inverse Distance Method, ID2(Inverse Square Distance Method), and Kr(General Covariance Kriging Method), after letting miss rainfall data to the observed data. Second, we reviewed the runoff characteristics of the distributed runoff model according to the estimated spatial rainfall. On the basis of Yuseong water level station, we select the target basin as Gabchun watershed. We assumed 1 point or 2 point of the 6 rainfall gauge stations in watershed were missed. We applied the spatial rainfall distributed by Kr to Hy-GIS GRM, distributed runoff model. When 1 point rainfall data is missed, Kr is superior to others in point rainfall estimation and runoff estimation of Hy-GIS GRM. However, in case rainfall data of 2 points is missed, all of three methods did not give suitable result for them. In conclusion, Kr showed better applicability than other estimated methods if rainfall's data less than 2 points is missed.

• Journal of the Korean Institute of Intelligent Systems
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• 제19권5호
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• pp.648-654
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• 2009

In this paper, a method called bilateral diagonal 2DLDA is proposed for face recognition. Two methods called Dia2DPCA and Dia2DLDA were suggested to reserve the correlations between the variations in the rows and columns of diagonal images. However, these methods work in the row direction of these images. A row-directional projection matrix can be obtained by calculating the between-class and within-class covariance matrices making an allowance for the column variation of alternative diagonal face images. In addition, column-directional projection matrix can be obtained by calculating the between-class and within-class covariance matrices making an allowance for the row variation in diagonal images. A bilateral projection scheme was applied using left and right multiplying projection matrices. As a result, the dimension of the feature matrix and computation time can be reduced. Experiments carried out on an ORL face database show that the proposed method with three different distance measures, namely, Frobenius, Yang and AMD, is more accurate than some methods, such as 2DPCA, B2DPCA, 2DLDA, etc.

• Korean Journal of Agricultural and Forest Meteorology
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• 제11권1호
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• pp.19-26
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• 2009

The standardization of eddy-covariance data processing is essential for the analysis and synthesis of vast amount of data being accumulated through continuous observations in various flux measurement networks. End users eventually benefit from the open and transparent standardization protocol by clear understanding of final products such as evapotranspiration and gross primary productivity. In this paper, we briefly introduced KoFlux efforts to standardize data processing methodologies and then estimated uncertainties of surface fluxes due to different processing methods. Based on our scrutiny of the data observed at Gwangneung KoFlux site, net ecosystem exchange and ecosystem respiration were sensitive to the selection of different processing methods. Gross primary production, however, was consistent within errors due to cancellation of the differences in NEE and Re, emphasizing that independent observation of ecosystem respiration is required for accurate estimates of carbon exchange. Nocturnal soil evaporation was small and thus the annually integrated evapotranspiration was not sensitive to the selection of different data processing methods. The implementation of such standardized data processing protocol to AsiaFlux will enable the establishment of consistent database for validation of models of carbon cycle, dynamic vegetation, and land-atmosphere interaction at regional scale.

• Korean Journal of Environmental Biology
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• 제36권4호
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• pp.507-516
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• 2018

The closed chamber method, which is one of the most commonly used method for measuring greenhouse gases produced in rice paddy fields, has limitations in measuring dynamic $CH_4$ flux with spatio-temporal constrains. In order to deal with the limitation of the closed chamber method, some studies based on open-path of eddy covariance method have been actively conducted recently. The aim of this study was to compare the $CH_4$ fluxes measured by open-path and closed chamber method in the paddy rice fields. The open-path, one of the gas ($CO_2$, $CH_4$ etc.) analysis methods, is technology where a laser beam is emitted from the source passes through the open cell, reflecting multiple times from the two mirrors, and then detecting. The $CH_4$ emission patterns by these two methods during rice cultivation season were similar, but the total $CH_4$ emission measured by open-path method were 31% less than of the amount measured by closed chamber. The reason for the difference in $CH_4$ emission was due to overestimation by closed chamber and underestimation by open-path. The closed chamber method can overestimate $CH_4$ emissions due to environmental changes caused by high temperature and light interruption by acrylic partition in chamber. On the other hand, the open-path method for eddy covariance can underestimate its emission because it assumes density fluctuations and horizontal homogeneous terrain negligible However, comparing $CH_4$ fluxes at the same sampling time (AM 10:30-11:00, 30-min fluxes) showed good agreements ($r^2=0.9064$). The open-path measurement technique is expected to be a good way to compensate for the disadvantage of the closed chamber method because it can monitor dynamic $CH_4$ fluctuation even if data loss is taken into account.

• Communications for Statistical Applications and Methods
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• 제27권3호
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• pp.377-383
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• 2020

The likelihood-based inference in a nonlinear generalized mixed model often requires computing moments of truncated multivariate normal random variables. Many methods have been proposed for the computation using a recurrence relation or the moment generating function; however, these methods rely on high dimensional numerical integrations. The numerical method is known to be inefficient for high dimensional integral in accuracy. Besides the accuracy, the methods demand too much computing time to use them in practical analyses. In this note, a moment calculation method is proposed under an assumption of a certain covariance structure that occurred mostly in generalized mixed models. The method needs only low dimensional numerical integrations.