• Journal of Gastric Cancer
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• v.5 no.2
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• pp.113-119
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• 2005

Purpose: Some controversies exist over the prognostic values of lymphatic, venous, and neural invasion in patients with gastric cancer. This study was conducted to confirm the prognostic values of these histopathologic factors in gastric cancer patients who received a gastrectomy. Materials and Methods: Data for clinicopathologic factors and clinical outcomes were collected retrospectively from the medical records of 1,018 gastric cancer patients who received a gastrectomy at Yeungnam University Medical Center between January 1995 and December 1999. A statistical analysis was done using the SPSS program for Windows (Version 10.0, SPSS Inc., USA). The Kaplan-Meier method was used for the survival analysis. Prognostic factors were analyzed by using a multivariate analysis with Cox proportional hazard regression model. Results: Ages ranged from 21 to 79 (median age, 56). A univariate analysis revealed that age, tumor size, location, gross type, depth of invasion, extent of gastrectomy or lymph node dissection, lymph node metastasis, distant metastasis, lymphatic invasion, venous invasion, neural invasion, pathologic stage, histologic type, and curability of surgery had statistical significance. Among these factors, lymph node metastasis, curability of surgery, neural invasion, lymphatic invasion, and depth of invasion were found to be independent prognostic factors by using a multivariate analysis. Venous invasion showed no prognostic value in the multivariate analysis. Conclusion: Neural invasion and lymphatic invasion are useful parameters in determining a prognosis for gastric cancer patients.

• Journal of Korea Water Resources Association
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• v.41 no.5
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• pp.517-525
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• 2008

Regional rainfall quantile depends on the identification of hydrologically homogeneous regions. Various variables relevant to precipitation can be used to form regions. Since the type and number of variables may lead to improve the efficiency of partitioning, it is important to select those precipitation related variables, which represent most of the information from all candidate variables. Multivariate analysis techniques can be used for this purpose. Procrustes analysis which can decrease the dimension of variables based on their correlations, are applied in this study. 42 rainfall related variables are decreased into 21 ones by Procrustes analysis. Factor analysis is applied to those selected variables and then 5 factors are extracted. Fuzzy-c means technique classifies 68 stations into 6 regions. As a result, the GEV distributions are fitted to 6 regions while the lognormal and generalized logistic distributions are fitted to 5 regions. For the comparison purpose with previous results, rainfall quantiles based on generalized logistic distribution are estimated by at-site frequency analysis, index flood method, and regional shape estimation method.

• Journal of Korean Ophthalmic Optics Society
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• v.14 no.4
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• pp.11-17
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• 2009

Purpose: The purpose of this study is to provide an empirical overview of the import patterns of the Eyeglasses and Contactlens industry. Methods: This study used an Engle-Granger cointegration technique and Johansen's multivariate cointegraion methodology test to check the stationarity of the model. This paper also applies Rolling regression to our model, indicating that Eyeglasses and Contact Lens import is endogenous to the economic variable. Results: The empirical results show how the import in Eyeglasses and Contact Lens is related to the economic variables. Conclusions: This paper shows how the import of Eyeglasses and Contactlens is influenced by economic variables, such as exchange rate and industrial product, and seasonal factors.

• Journal of Korean Society on Water Environment
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• v.23 no.1
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• pp.161-168
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• 2007

The main aim of this work is focus on the Geum river water quality evaluation of pollution data obtained by monitoring measurement during the period 2001-2005. The complex data matrix 19 (entire monitoring stations)*13 (parameters), 60 (month)*13 (parameters) and 20 (season)*13 (parameters) were treated with different multivariate techniques such as factor analysis/principal component analysis (FA/PCA). FA/PCA identified two factor (19*13) classified pollutant Loading factor (BOD, COD, pH, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P, Chl-a), seasonal factor (water temp, SS) and three Factor (60*13, 20*13) classified pollutant Loading factor (BOD, COD, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P), seasonal factor (water temp, SS) and metabolic factor (Chl-a, pH). Loadings of pollutant factor is potent influence main factor in the Geum river which is explained by loadings of pollutant factor at whole sampling stations (71.16%), month (52.75%) and season (56.57%) of main water quality stations. Result of this study is that pollutant loading factor is affected at Gongju 1, 2, Buyeo 1, 2, Gangkyeong, Yeongi stations by entire stations and entire month (Gongju 1, Cheongwon stations), April, May, July and August (buyeo 1) by month. Also the pollutant Loading factor is season gives an influence in winter (Gongju 1, buyeo 1) from main sampling stations, but Cheongwon characteristic is non-seasonal influenced. This study presents necessity and usefulness of multivariate statistic techniques for evaluation and interpretation of large complex data set with a view to get better information data effective management of water sources.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1065-1074
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• 2011

During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.

• Radiation Oncology Journal
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• v.20 no.2
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• pp.130-138
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• 2002

Purpose : This study analyzed the prognostic factors affecting the survival rate and evaluated the role of radiation therapy in muscle-invading bladder cancer. Materials and Methods : Twenty eight patients with bladder cancer who completed planned definitive radiotherapy in the Departments of Therapeutic Radiology and Urology, Chonnam National University Hospital between Jan. 1986 to Dec. 1998 were retrospectively analyzed. The reviews were peformed based on the patients' medical records. There were 21 males and 7 females in this study. The median of age was 72 years old ranging from 49 to 84 years. All patients were confirmed as having transitional cell carcinoma with histological grade 1 in one patient, grade 2 in 15, grade 3 in 9, and uninformed in 3. Radiation therapy was peformed using a linear accelerator with 6 or 10 MV X-rays. Radiation was delivered daily with a 1.8 or 2.0 Gy fraction size by 4 ports (anterior-posterior, both lateral, alternatively) or 3 ports (Anterior and both lateral). The median radiation dose delivered to the isocenter of the target volume was 61.24 Gy ranging from 59 to 66.6 Gy. The survival rate was calculated by the Kaplan-Meier method. Multivariate analysis was peformed on the prognostic factors affecting the survival rate. Results : The survival rate was $76\%,\;46\%,\;33\%,\;33\%$ at 1, 2, 3, 5 years, respectively, with 19 months of median survival. The potential factors of age (less than 70 years vs above 70), sex, diabetes mellitus, hypertension, hydronephrosis, 1-stage (T3a vs T3b), TUR, chemotherapy, total duration of radiotherapy, radiation dose (less than 60 Gy vs above 60 Gy), and the treatment response were investigated with uniand multivariate analysis. Un univariate analysis, the T-stage (p=0.078) and radiation dose (p=0.051) were marginally significant, and the treatment response (p=0.011) was a statistically significant factor on the survival rate. Multivariate analysis showed there were no significant prognostic factors affecting the survival rate. Conclusion : The treatment response and radiation dose are suggested as th은 statistically significant factors affecting the survival rate of muscle invasive bladder cancer. A Further prospective randomized study is needed to confirm these prognostic factors.

• Information Systems Review
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• v.26 no.2
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• pp.45-57
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• 2024

This study develops a deep learning-based methodology for predicting Crude Palm Oil (CPO) prices. Palm oil is an essential resource across various industries due to its yield and economic efficiency, leading to increased industrial interest in its price volatility. While numerous studies have been conducted on palm oil price prediction, most rely on time series forecasting, which has inherent accuracy limitations. To address the main limitation of traditional methods-the absence of stationarity-this research introduces a novel model that uses the ratio of future prices to current prices as the dependent variable. This approach, inspired by return modeling in stock price predictions, demonstrates superior performance over simple price prediction. Additionally, the methodology incorporates the consideration of lag values of independent variables, a critical factor in multivariate time series forecasting, to eliminate unnecessary noise and enhance the stability of the prediction model. This research not only significantly improves the accuracy of palm oil price prediction but also offers an applicable approach for other economic forecasting issues where time series data is crucial, providing substantial value to the industry.

• Journal of Chest Surgery
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• v.37 no.3
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• pp.235-244
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• 2004

Anatomic correction of the transposition of the great arteries (TGA) or Taussig-Bing anomaly by means of the arterial switch operation is now accepted as the therapeutic method of choice. This retrospective study was conducted to evaluate the risk factors for operative deaths and the efficacy of technical modification of the coronary transfer. 85 arterial switch operations for TGA or Taussig-Bing anomaly which were performed by one surgeon from 1994 to July 2002 at Dong-A university hospital were included in this retrospective study Multivariate analysis of perioperative variables for operative mortality including technical modification of the coronary transfer was peformed. Overall postoperative hospital mortality was 20.0% (17/85). The mortality before 1998 was 31.0% (13/42), but reduced to 9.3% (4/43) from 1998. The mortality in the patients with arch anomaly was 61.5% (8/13), but 12.5% (9/72) in those without arch anomaly. In patients who underwent an open coronary reimplantation technique, the operative mortality was 28.1% (18/64), but 4.8% (1/21) in patients undergoing a technique of reimplantation coronary buttons after neoarotic reconstruction. Risk factors for operative death from multivariated analysis were cardiopulmonary bypass time ($\geq$ 250 minutes), aortic cross-clamping time ($\geq$ 150 minutes), aortic arch anomaly, preoperative event, and open coronary reimplantation technique. Operative mortality has been reduced with time. Aortic arch anomaly and preoperative events were important risk factors for postoperative mortality. However atypical coronary artery patterns did not work as risk factors. We think that the technical modification of coronary artery transfer played an important role in reducing the postoperative mortality of arterial switch operation.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.291-299
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• 2008

This paper discusses model building for repeated binary response data with different time-dependent covariates each occasion. Since repeated measurements data are having correlated structure, weighed least squares(WLS) methodology is applied. Repeated measures designs are usually having different sizes of experimental units like split-plot designs. However repeated measures designs differ from split-plot designs in that the levels of one or more factors cannot be randomly assigned to one or more of the sizes of experimental units in the experiment. In this case, the levels of time cannot be assigned at random to the time intervals. Because of this nonrandom assignment, the errors corresponding to the respective experimental units may have a covariance matrix. So, the estimates of effects included in a suggested logit model are obtained by using covariance structures.

• Clean Technology
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• v.18 no.1
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• pp.31-37
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• 2012

Biomass has become a major feedstock for bioenergy and other bio-based products because of its renewability and environmental benefits. Various researches have been done in the prediction of crucial characteristics of biomass, including the active utilization of spectroscopy data. Near infrared (NIR) spectroscopy has been widely used because of its attractive features: it's non-destructive and cost-effective producing fast and reliable analysis results. This work developed the multivariate statistical scheme for predicting weight loss profiles based on the utilization of NIR spectra data measured for six lignocellulosic biomass types. Wavelet analysis was used as a compression tool to suppress irrelevant noise and to select features or wavelengths that better explain NIR data. The developed scheme was demonstrated using real NIR data sets, in which different prediction models were evaluated in terms of prediction performance. In addition, the benefits of using right pretreatment of NIR spectra were also given. In our case, it turned out that compression of high-dimensional NIR spectra by wavelet and then PLS modeling yielded more reliable prediction results without handling full set of noisy data. This work showed that the developed scheme can be easily applied for rapid analysis of biomass.