• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.923-932
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• 2014

The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.

• Journal of Korean Society on Water Environment
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• v.26 no.2
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• pp.252-260
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• 2010

The point rainfall measurements need to be converted to the areal rainfall by means of mean areal precipitation (MAP) estimation methods. And it is not appropriate to evaluate the areal rainfall with constant drift because of the geomorphological influences to rainfall field. Non-stationarity should be applied to the estimation of the areal rainfall, therefore, to consider these effects. Kriging methods with special functional would be a suitable tool in this case. Generalized covariance Kriging method is the most developed one among different Kriging methods. From this point of view this study performs the analysis of its applicability to distributed runoff model. For these purpose, distributed rainfall was created by Thiessen and Kriging method. And distributed rainfall of each method was applied into HyGIS-GRM. The result of applying, Runoff was different in the rainfall data form. Therefore, To apply Kriging method with physical meaning is that it is the useful method as distributed rainfall-runoff model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.475-480
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• 2005

Information of the maximum Doppler frequency enable to optimize many channel-adaptive techniques and radio resource management methods for mobile radio communication systems. In this paper, we propose two maximum Doppler frequency estimators which are based on the level crossing rate(LCR) and the covariance function (COV). To eliminate the effect of additive noise, we analyze the conditions for the estimators independent of the signal-to-noise ratio(SNR) and implement the conditions with a simple downsampling process. The proposed methods achieve good SNR-independent performance.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.435-448
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• 2012

Evapotranspiration (ET) including evaporation from a land surface and transpiration from photosynthesis of vegetation is a sensitive hydrological factor with outer circumstances. Though both direct measurements with an evaporation pan and a lysimeter, and empirical methods using eddy covariance technique and the Bowen ratio have been widely used to observe ET accurately, they have a limitation that the observation can stand for the exact site, not for an area. In this study, remote sensing technique is adopted to compensate the limitation of ground observation using the Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral sensor mounted on Terra satellite. We improved to evapotranspiration model based on remote sensing (Mu et al., 2007) and estimated Penman-Monteith evapotranspiration considering regional characteristics of Korea that was using only MODIS product. We validated evapotranspiration of Sulma (SMK)/Cheongmi (CFK) flux tower observation and calculation. The results showed high correlation coefficient as 0.69 and 0.74.

• Atmosphere
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• v.26 no.3
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• pp.473-482
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• 2016

An accurate determination of turbulent fluxes over an urban area is a challenging task due to its morphological diversity and associated flow complexity. In this study, an eddy covariance (EC) method is applied over a highly heterogeneous urban area in a small city (Gongju), South Korea to investigate the quantitative influence of 'coordinate tilt' in determining the turbulent fluxes of sensible heat, latent heat, momentum, and carbon dioxide mass. Two widely-used coordinate transform methods are adopted and applied to eight directional sections centered on the site to analyze a 1-year period EC measurement obtained from the urban site: double rotation (DR) and planar fit (PF) transform. The results show that mean streamline planes determined by the PF method are distinguished from the sections, representing morphological heterogeneity of the site. The sectional pitch angles determined by the DR method also compare well with those in the PF method. Both the PF and DR methods show large variabilities in the determined streamline planes at each directional section, implying that flow patterns may form in a complicate way due to the surface heterogeneity. Resulting relative differences of the turbulent fluxes, defined by $(F_{DR}-F_{PF})/F_{DR}$, are found on average +13% in sensible heat flux, +21% in latent heat flux, +37% in momentum flux, and +26% in carbon dioxide mass flux, which are larger values than those reported previously for fairly homogeneous natural sites. The fractional differences depend significantly on wind direction, showing larger differences in northerly winds at the measurement site. It is also found that the relative fractional differences are negatively correlated with the mean wind speed at both stable/unstable atmospheric conditions. These results imply that EC turbulent fluxes determined over heterogeneous urban areas should be carefully interpreted with considering the uncertainty due to 'coordinate tilt' effect in their applications.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.60-65
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• 2018

In this paper, a covariance matrix and modulogram are proposed for realizing amphibian sound classification using CNN (Convolutional Neural Network). First of all, a database is established by collecting amphibians sounds including endangered species in natural environment. In order to apply the database to CNN, it is necessary to standardize acoustic signals with different lengths. To standardize the acoustic signals, covariance matrix that gives distribution information and modulogram that contains the information about change over time are extracted and used as input to CNN. The experiment is conducted by varying the number of a convolutional layer and a fully-connected layer. For performance assessment, several conventional methods are considered representing various feature extraction and classification approaches. From the results, it is confirmed that convolutional layer has a greater impact on performance than the fully-connected layer. Also, the performance based on CNN shows attaining the highest recognition rate with 99.07 % among the considered methods.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1271-1278
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• 2006

Recently, a profit analysis in hospital management is considered as an important marketing concept. When spatial variability is presented, we must analyze the hospital data with spatial statistical methods. In this study, we present a regression model using spatial covariance for adjustment. And we compare the nonspatial model with spatial model.

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.37-42
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• 1996

Maximum likelihood estimation of Cholesky decomposition is considered under normality assumption. It is shown that maximum liklihood estimation gives a Cholesky decomposition of the sample covariance matrix. The joint distribution of the maximum likelihood estimators is derived. The ussual algorithm for a Cholesky decomposition is shown to be equivalent to a maximumlikelihood estimation of a Cholesky root when the underlying distribution is a multivariate normal one.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.114-125
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• 2003

This paper proposes multivariate process capability indices (PCIs) for skewed populations using $T^2$rand modified process region approaches. The proposed methods are based on the multivariate version of a weighted standard deviation method which adjusts the variance-covariance matrix of quality characteristics and approximates the probability density function using several multivariate Journal distributions with the adjusted variance-covariance matrix. Performance of the proposed PCIs is investigated using Monte Carlo simulation, and finite sample properties of the estimators are studied by means of relative bias and mean square error.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.701-718
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• 2006

In cancer microarray experiments, the experimenter or patient which is nested in each experimenter often shows quite heterogeneous error variability, which should be estimated for identifying a source of variation. Our study describes a Bayesian method which utilizes clinical information for identifying a set of DE genes for the class of subtypes as well as assesses and examines the experimenter effect and patient effect which is nested in each experimenter as a source of variation. We propose a Bayesian multilevel mixed effect model based on analysis of covariance (ANACOVA). The Bayesian multilevel mixed effect model is a combination of the multilevel mixed effect model and the Bayesian hierarchical model, which provides a flexible way of defining a suitable correlation structure among genes.