• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.99-112
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• 2016

Asymmetric jump-diffusion models are effectively used to model the dynamic behavior of asset prices with abrupt asymmetric upward and downward changes. However, the estimation of their extension to the multivariate asymmetric jump-diffusion model has been hampered by the analytically intractable likelihood function. This article confronts the problem using a data augmentation method and proposes a new Bayesian method for a multivariate asymmetric Laplace jump-diffusion model. Unlike the previous models, the proposed model is rich enough to incorporate all possible correlated jumps as well as mention individual and common jumps. The proposed model and methodology are illustrated with a simulation study and applied to daily returns for the KOSPI, S&P500, and Nikkei225 indices data from January 2005 to September 2015.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.698-701
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• 2002

Subspace analysis (which includes PCA) seeks for feature subspace (which corresponds to the eigenspace), given multivariate input data and has been widely used in computer vision and pattern recognition. Typically data space belongs to very high dimension, but only a few principal components need to be extracted. In this paper I present a fast sequential algorithm for subspace analysis or tracking. Useful behavior of the algorithm is confirmed by numerical experiments.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.229-231
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• 2006

NLPCA(Nonlinear Principal Component Analysis) is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(Auto Associative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from sensor network is executed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1543-1561
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• 2020

Taxonomy of human brain activity is a complicated rather challenging procedure. Due to its multifaceted aspects, including experiment design, stimuli selection and presentation of images other than feature extraction and selection techniques, foster its challenging nature. Although, researchers have focused various methods to create taxonomy of human brain activity, however use of multivariate pattern analysis (MVPA) for image recognition to catalog the human brain activities is scarce. Moreover, experiment design is a complex procedure and selection of image type, color and order is challenging too. Thus, this research bridge the gap by using MVPA to create taxonomy of human brain activity for different categories of images, both colored and gray scale. In this regard, experiment is conducted through EEG testing technique, with feature extraction, selection and classification approaches to collect data from prequalified criteria of 25 graduates of University Technology PETRONAS (UTP). These participants are shown both colored and gray scale images to record accuracy and reaction time. The results showed that colored images produces better end result in terms of accuracy and response time using wavelet transform, t-test and support vector machine. This research resulted that MVPA is a better approach for the analysis of EEG data as more useful information can be extracted from the brain using colored images. This research discusses a detail behavior of human brain based on the color and gray scale images for the specific and unique task. This research contributes to further improve the decoding of human brain with increased accuracy. Besides, such experiment settings can be implemented and contribute to other areas of medical, military, business, lie detection and many others.

• Toxicological Research
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• v.28 no.2
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• pp.81-91
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• 2012

Many risk factors exist for chemotherapy-induced nausea and vomiting (CINV). This study utilized a multivariate projection technique to identify which risk factors were predictive of CINV in clinical practice. A single-centre, prospective, observational study was conducted from January 2007~July 2010 in Singapore. Patients were on highly (HECs) and moderately emetogenic chemotherapies with/without radiotherapy. Patient demographics and CINV risk factors were documented. Daily recording of CINV events was done using a standardized diary. Principal component (PC) analysis was performed to identify which risk factors could differentiate patients with and without CINV. A total of 710 patients were recruited. Majority were females (67%) and Chinese (84%). Five risk factors were potential CINV predictors: histories of alcohol drinking, chemotherapy-induced nausea, chemotherapy-induced vomiting, fatigue and gender. Period (ex-/current drinkers) and frequency of drinking (social/chronic drinkers) differentiated the CINV endpoints in patients on HECs and anthracycline-based, and XELOX regimens, respectively. Fatigue interference and severity were predictive of CINV in anthracycline-based populations, while the former was predictive in HEC and XELOX populations. PC analysis is a potential technique in analyzing clinical population data, and can provide clinicians with an insight as to what predictors to look out for in the clinical assessment of CINV. We hope that our results will increase the awareness among clinician-scientists regarding the usefulness of this technique in the analysis of clinical data, so that appropriate preventive measures can be taken to improve patients' quality of life.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8567-8571
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• 2016

Background: The Cox model is known as one of the most frequently-used methods for analyzing survival data. However, in some situations parametric methods may provide better estimates. In this study, a Weibull parametric model was employed to assess possible prognostic factors that may affect the survival of patients with breast cancer. Materials and Methods: We studied 438 patients with breast cancer who visited and were treated at the Cancer Research Center in Shahid Beheshti University of Medical Sciences during 1992 to 2012; the patients were followed up until October 2014. Patients or family members were contacted via telephone calls to confirm whether they were still alive. Clinical, pathological, and biological variables as potential prognostic factors were entered in univariate and multivariate analyses. The log-rank test and the Weibull parametric model with a forward approach, respectively, were used for univariate and multivariate analyses. All analyses were performed using STATA version 11. A P-value lower than 0.05 was defined as significant. Results: On univariate analysis, age at diagnosis, level of education, type of surgery, lymph node status, tumor size, stage, histologic grade, estrogen receptor, progesterone receptor, and lymphovascular invasion had a statistically significant effect on survival time. On multivariate analysis, lymph node status, stage, histologic grade, and lymphovascular invasion were statistically significant. The one-year overall survival rate was 98%. Conclusions: Based on these data and using Weibull parametric model with a forward approach, we found out that patients with lymphovascular invasion were at 2.13 times greater risk of death due to breast cancer.

• Journal of Gastric Cancer
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• v.23 no.3
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• pp.462-475
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• 2023

Purpose: This study aimed to analyze the incidence and risk factors of complications following gastric cancer surgery in Korea and to compare the correlation between hospital complications based on the annual number of gastrectomies performed. Materials and Methods: A retrospective analysis was conducted using data from 12,244 patients from 64 Korean institutions. Complications were classified using the Clavien-Dindo classification (CDC). Univariate and multivariate analyses were performed to identify the risk factors for severe complications. Results: Postoperative complications occurred in 14% of the patients, severe complications (CDC IIIa or higher) in 4.9%, and postoperative death in 0.2%. The study found that age, stage, American Society of Anesthesiologists (ASA) score, Eastern Cooperative Oncology Group (ECOG) score, hospital stay, approach methods, and extent of gastric resection showed statistically significant differences depending on hospital volumes (P<0.05). In the univariate analysis, patient age, comorbidity, ASA score, ECOG score, approach methods, extent of gastric resection, tumor-node-metastasis (TNM) stage, and hospital volume were significant risk factors for severe complications. However, only age, sex, ASA score, ECOG score, extent of gastric resection, and TNM stage were statistically significant in the multivariate analysis (P<0.05). Hospital volume was not a significant risk factor in the multivariate analysis (P=0.152). Conclusions: Hospital volume was not a significant risk factor for complications after gastric cancer surgery. The differences in the frequencies of complications based on hospital volumes may be attributed to larger hospitals treating patients with younger age, lower ASA scores, better general conditions, and earlier TNM stages.

• Water for future
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• v.24 no.4
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• pp.59-66
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• 1991

Abstract The purpose of the synthetic of monthly river flows based on the short-term observed data by means of multivariate stochastic models is to provide abundunt input data to the water resources systems of which the system performance and operation policy are to be determined beforehand. In this study, multivariate Thomas-Fiering and Matalas models for synthetic generation based on stream flows in neihboring basin were employed to check if it can be applide in the modeling of monthly flows. Statistical parameters estimated by Method of Moment and Fourier Series Analysis respectively were reproduced for statistical features. For comparisons the statistical parameters of the generated monthly flow by each model were compared with those of the observed monthly flows. Results of this study suggest that the application of Matalas model for synthetic generation of monthly river flows can be adapted.

• Journal of Korean Society for Quality Management
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• v.24 no.4
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• pp.168-189
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• 1996

In order for the corporation to get a good image from the customers it should consider several variables, but especially important are corproate ethics and customer satisfaction through quality improvement. Standard multivariate data analysis can be applied to find out the importance of customer satisfaction and corporate ethics as influence factors in the corporate competitive strategy. When applying this Methodology, multivariate normal distributions density function and the identical covariance between groups assumptions have to be satisfied. By using the evaluation result from a small number of specialists in an attempt to decide on the strategical factors that will create a better company image than its competitor, if it chooses to use statistical discriminant analysis method, it would be difficult to satisfy the two assumptions mentioned above. This thesis introduces discriminant analysis method that uses LP/GP effectively which is applicable to this particular situation.

• Journal of Korean Society for Atmospheric Environment
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• v.9 no.4
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• pp.278-287
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• 1993

In order to calssify smog type durnig the summer season in Seoul, air Quality and meterorological data were analyzed by multivariate analysis. Among 15 variables relating to visibility, 10 variables were selected by multiple regression analysis for clustering of smog types; total suspended particle, sulfur dioxide, ozone, ntrogen dioxide, total hydrocarbon, south-north wind component, ralative humidity, precipitable water, mixing height and air temperature. Somg types were grouped into three clusters using cubic clustering criterion and the mumbers of days in each cluster were contained 74, 28 and 16 days. Each cluster was seperated clearly by sulfur dioxide, precipitable water and air teperature. The first cluster was representative of high ozone concentration and prevailing meterological conditions for ozone formation. Therefore, visibility in the first cluster was considered to be affected by photochemical smog. The third cluster showed characteristics of sulphurous smog type due to the higher concentration of primary pollutant, based on the dry condition than that in another cluster. On the other hand, the characteristic of the second cluster was not relatively clear, but considered to be in an intermediate characteristic between photochemical smog and sulphurous smog type.