• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.557-573
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• 2015

In this paper we develop a Bayesian inference for a multiplicative double seasonal autoregressive (DSAR) model by implementing a fast, easy and accurate Gibbs sampling algorithm. We apply the Gibbs sampling to approximate empirically the marginal posterior distributions after showing that the conditional posterior distribution of the model parameters and the variance are multivariate normal and inverse gamma, respectively. The proposed Bayesian methodology is illustrated using simulated examples and real-world time series data.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.999-1007
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• 2006

The semiparametric gamma frailty models have been often used for multivariate survival analysis because they give an explicit marginal likelihood. The commonly used estimation procedure is the profile likelihood method based on marginal likelihood, which provides the same parameter estimates as the EM algorithm. In this paper we show in finite samples the standard profile-likelihood method can lead to an underestimation of parameters, particularly for the frailty parameter. To overcome this problem, we propose an adjusted profile-likelihood method. For the illustration a numerical example and a small-sample simulation study are presented.

• Journal of the Korean Chemical Society
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• v.36 no.5
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• pp.720-726
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• 1992

Multivariate analysis such as factor analysis was applied to interpret multivariate data, which were obtained from the overlapped X-ray fluorescence spectra. X-ray fluorescence spectra of 11 reference samples were obtained by the wavelength dispersive spectrometer at a specified range of angle such as $33.50∼34.50^{\circ}$. The data matrix was made from the spectra of 8 samples. The results of abstract factor analysis gave three factors. By the target testing with 8 elements contained in the reference samples, the three factors were found to be Pb, As and Cu. The concentration of these elements in the test samples was determined by target transformation factor analysis regardless overlapping individual peaks.

• Journal of Korean Society on Water Environment
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• v.37 no.6
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• pp.520-530
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• 2021

Multivariate statistical analysis and an environmental hydrological model were applied for investigating the causes of water pollution and providing best management practices for water quality improvement in urban and agricultural watersheds. Principal component analysis (PCA) and cluster analysis (CA) for water quality time series data show that chemical oxygen demand (COD), total organic carbon (TOC), suspended solids (SS) and total phosphorus (T-P) are classified as non-point source pollutants that are highly correlated with river discharge. Total nitrogen (T-N), which has no correlation with river discharge and inverse relationship with water temperature, behaves like a point source with slow and consistent release. Biochemical oxygen demand (BOD) shows intermediate characteristics between point and non-point source pollutants. The results of the PCA and CA for the spatial water quality data indicate that the cluster 1 of the watersheds was characterized as upstream watersheds with good water quality and high proportion of forest. The cluster 3 shows however indicates the most polluted watersheds with substantial discharge of BOD and nutrients from urban sewage, agricultural and industrial activities. The cluster 2 shows intermediate characteristics between the clusters 1 and 3. The results of hydrological simulation program-Fortran (HSPF) model simulation indicated that the seasonal patterns of BOD, T-N and T-P are affected substantially by agricultural and livestock farming activities, untreated wastewater, and environmental flow. The spatial analysis on the model results indicates that the highly-populated watersheds are the prior contributors to the water quality degradation of the river.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.301-303
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• 2002

Subspace analysis is a popular method for multivariate data analysis and is closely related to factor analysis and principal component analysis (PCA). In the context of image processing (especially positron emission tomography), all data points are nonnegative and it is expected that both basis images and factors are nonnegative in order to obtain reasonable result. In this paper We present a sequential EM algorithm for rectified subspace analysis (subspace in nonnegativity constraint) and apply it to dynamic PET image analysis. Experimental results show that our proposed method is useful in dynamic PET image analysis.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.271-281
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• 2013

This paper considers a problem of classification of variable stars based on functional data analysis. For a better understanding of galaxy structure and stellar evolution, various approaches for classification of variable stars have been studied. Several features that explain the characteristics of variable stars (such as color index, amplitude, period, and Fourier coefficients) were usually used to classify variable stars. Excluding other factors but focusing only on the curve shapes of variable stars, Deb and Singh (2009) proposed a classification procedure using multivariate principal component analysis. However, this approach is limited to accommodate some features of the light curve data that are unequally spaced in the phase domain and have some functional properties. In this paper, we propose a light curve estimation method that is suitable for functional data analysis, and provide a classification procedure for variable stars that combined the features of a light curve with existing functional data analysis methods. To evaluate its practical applicability, we apply the proposed classification procedure to the data sets of variable stars from the project STellar Astrophysics and Research on Exoplanets (STARE).

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.1
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• pp.97-101
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• 2012

Objective: With the background of aging population in China and advances in clinical medicine, the amount of operations on old patients increases correspondingly, which imposes increasing challenges to critical care medicine and geriatrics. The study was designed to describe information on the length of ICU stay from a single institution experience of old critically ill gastric cancer patients after surgery and the framework of incorporating data-mining techniques into the prediction. Methods: A retrospective design was adopted to collect the consecutive data about patients aged 60 or over with a gastric cancer diagnosis after surgery in an adult intensive care unit in a medical university hospital in Shenyang, China, from January 2010 to March 2011. Characteristics of patients and the length their ICU stay were gathered for analysis by univariate and multivariate Cox regression to examine the relationship with potential candidate factors. A regression tree was constructed to predict the length of ICU stay and explore the important indicators. Results: Multivariate Cox analysis found that shock and nutrition support need were statistically significant risk factors for prolonged length of ICU stay. Altogether, eight variables entered the regression model, including age, APACHE II score, SOFA score, shock, respiratory system dysfunction, circulation system dysfunction, diabetes and nutrition support need. The regression tree indicated comorbidity of two or more kinds of shock as the most important factor for prolonged length of ICU stay in the studied sample. Conclusions: Comorbidity of two or more kinds of shock is the most important factor of length of ICU stay in the studied sample. Since there are differences of ICU patient characteristics between wards and hospitals, consideration of the data-mining technique should be given by the intensivists as a length of ICU stay prediction tool.

• ETRI Journal
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• v.32 no.2
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• pp.302-311
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• 2010

In this study, we analyzed consumers' choices of various broadcasting and telecommunication convergence services and time consumption for chosen services by using survey data. A multivariate probit model was used to model consumers' choices of various broadcasting and telecommunication convergence services, and an ordered probit model was used to model consumers' time consumption for chosen services. Factors affecting consumers' choices and time-consumption behavior were identified, and simulation results of market competition and substitution were obtained. Based on these results, it was found that for the time being, consumers are highly locked into existing broadcasting services and are likely to become more price-sensitive to the new broadcasting and telecommunication convergence services. Also, the ways in which individual characteristics affect choices and time consumption were found to be very diverse service by service.

• Proceedings of the Safety Management and Science Conference
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• 2005.11a
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• pp.300-310
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• 2005

Mahalanobis Taguchi System (MTS) is developed by Genishi Taguchi as a part of his quality engineering methodology. The basic idea of Taguchi's quality engineering is looking for the way of effectiveness of analyzing multivariate system. In the MTS, with the standardized variables of healthy normal data, Mahalanobis Distance(MD) calculated and that can be discriminate between normal and abnormal objects. If this discrimination process is successful, next step is optimization which is try to reduce number of attributes by neglecting less effective attributes to MD. Orthogonal Array (OA) and Signal to Noise ratio (S/N) are used to evaluate the amount contribution of each attribute to the MD. Wisconsin Breast Cancer study, from machining learning repository at University of California at Irvine, used for examining the discriminant ability of MTS.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.43-48
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• 1999

To clarify the motion characteristics of free-style Okinawan dance“Kachaasi”, first the subjective impression was quantitatively evaluated with semantic differential technique to cluster its types. Then, the contingency of joint rotation in shoulder, elbow and wrist joints was examined with multivariate autoregressive model. The time-series data of positions and angels of three joints were calculated according to the deforming conditions and shielding directions of the ring lights. As the results, in an excellent dancer, the motions of shoulder and elbow were highly synchronized and smoothly controlled. The low-frequency output of the shoulder and elbow were mutually interacted. Meanwhile, the wrist behaved independently of other joints' rotation.