• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.921-932
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• 2015

This study analyzed military personnel survivability in regards to offensive operations according to the scientific military training data of a reinforced infantry battalion. Scientific battle training was conducted at the Korea Combat Training Center (KCTC) training facility and utilized scientific military training equipment that included MILES and the main exercise control system. The training audience freely engaged an OPFOR who is an expert at tactics and weapon systems. It provides a statistical analysis of data in regards to state-of-the-art military training because the scientific battle training system saves and utilizes all training zone data for analysis and after action review as well as offers training control during the training period. The methodologies used the Cox PH modeling (which does not require parametric distribution assumptions) and decision tree modeling for survival data such as CART, GUIDE, and CTREE for richer and easier interpretation. The variables that violate the PH assumption were stratified and analyzed. Since the Cox PH model result was not easy to interpret the period of service, additional interpretation was attempted through univariate local regression. CART, GUIDE, and CTREE formed different tree models which allow for various interpretations.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.6
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• pp.137-147
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• 2016

The purpose of this study was to analyze the perception factors of a pedestrian environment that affect pedestrian environment satisfaction(PES) and determine the relationship between PES and the expected effects of health promotion. The targeted areas of study are neighborhood parks in Suseong-gu, Daegu city. First, regarding the results for the evaluation of pedestrian environment perception, 'Gentle slope' was rated the highest, while factors regarding pedestrian safety such as 'Lots of unpleasant elements', 'Risk from biking and motorcycling' and 'Many obstacles on sidewalks' were rated low. A stepwise regression analysis showed that factors such as 'Fresh air', 'Beautiful scenery', 'Continuity of the sidewalks', 'Various attractions', 'The shade of trees' and 'Lots of unpleasant elements' influenced the PES. Therefore, creating fresh air and shade trees by planting trees and removing unpleasant elements from pedestrian areas are important. Also, it is necessary to cultivate beautiful scenery and attractions through street improvement and improve the continuity of the sidewalks. Finally, in terms of path analysis, PES influenced the frequency of park use, the expected effects of physical and mental health promotion both directly and indirectly.

• Journal of Korean Society of Forest Science
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• v.78 no.3
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• pp.323-330
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• 1989

Pinass densiflora natural forests of four local forms in Korea were studies to investigate effective biomass estimation method. Dimension analysis was used and three allometric regression models, such as logWt=A+BlogD, logWt=$A+B1ogD^2H$ and 1ogWt=A+BlogD+ClogH were applied to estimate biomass, The most accurate estimation was made by the regression model of logWt=A+BlogD+ClogH where Wt is dry weight, D is diameter at breast height, and H is tree height. However, dry weights of cones and dead branches were remotely related to tree size factor, such as D and H. In the interest of practical use. generalized allometric regressions for all samples trees of four stands were computed and analysis of covariance was used to compare the allometric regressions among the four stands. Based on the test criteria applied in this study, significant differences were found in terms of error variance and regression intercept, not in terms of regression slope. These trends suggest a generalized biomass regression is not valid for accurate estimation over a range of four local form stands.

• Journal of Korean Society of Forest Science
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• v.66 no.1
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• pp.82-94
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• 1984

The variation of stomatal density and stomatal length of four species of oaks was studied for the purpose of examining the differences among populations and among individual trees within population. Nine populations of Quercus mongolica, four populations of Q. serrata and Q. variabilis respectively, and three populations of Q. acutissima were selected in the natural stands of oaks distributed through the whole country. Twelve leaves were sampled from each of 20 trees from each population. The length of 20 stomata and ten replications of stomatal density were measured from collodion replicas of each leaf under a microscope. Average stomatal densities and lengths ranged through $600-1000/mm^2$ and $19-26{\mu}m$ respectively in all of the species studied. The stomatal densities and lengths presented significant differences statistically at the level of 1 or 5% among populations and among individual trees within population in all the species. Quercus mongolica, especially, showed large variation among populations, while Q. variabilis did very narrow variation compared to the other species. The coefficients of variation of stomatal densities and lengths among individual trees within population exhibited small values of 3.7-12.0% and 1.4-5.3% respectively in all the populations of the species. The average stomatal densities of Q. mongolica showed statistically significant correlation of multiple correlation coefficient of $R_{df{\cdot}2.6}=0.868^*$ and multiple regression equation of $Y=0.041X_1(G.M.T.S.)+0.489X_2(G.M.H.S.)+22.37$ with the sum of growing season mean daily temperature and the sum of growing season mean daily humidity of the stand studied. However the average stomatal lengths showed no relation with the same meteological variables. The figures of frequency distribution of the measurements of leaves or the mean values of individual trees did not show normal distribution curves in some populations. The curves, as well as the results of ANOVA, exhibited the differences among populations.

• Journal of Korean Society of Transportation
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• v.24 no.1 s.87
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• pp.39-52
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• 2006

This study divided the large-scale traffic accident locations into its own characteristics by using Cluster Analysis. Also, Quantification II and Classification and Regression Tree methods were used enabling evaluation for the amount of affecting influence by the crash type. After these analyses, we tested the fitness of the results and suggested the simplification of the quantification index. With the results from the discussed procedure, obvious differences were observed by groups according to the characteristics of crash type from the Discrimination and Classification analysis of divided four groups. Thus, measures and supplementary measures for the traffic accidents could be suggested in groups systematically. However, a lot of missing values in variables caused a huge loss of data and made this study difficult for more detailed analysis, With this difficulty. recording mandatory log files with a standardized format is also recommended to Prevent this Problem in advance.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.53-63
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• 2008

It is commonly believed that disease of human or economic traits of livestock are caused not by single gene acting alone, but by multiple genes interacting with one an-other. This issue is difficult due to the limitations of parametric statistical method like as logistic regression for detection of gene effects that are dependent solely on interactions with other genes and with environmental exposures. Multifactor dimensionality reduction (MDR) nonparametric statistical method, to improve the identification of single nucleotide polymorphism (SNP) associated with the Hanwoo(Korean cattle) carcass cold weight, is applied and compared with ANOVA results.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.535-546
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• 2011

This study develops statistical models for the binary forecast of Asian dust days over South Korea in the winter season. For this study, we used three kinds of data; the rst one is the observed Asian dust days for a period of 31 years (1980 to 2010) as target values, the second one is four meteorological factors(near surface temperature, precipitation, snowfall, ground wind speed) in the source regions of Asian dust based on the NCEP reanalysis data and the third one is the large-scale climate indices. Four kinds of statistical models(multiple regression models, logistic regression models, decision trees, and support vector machines) are applied and compared based on skill scores(hit rate, probability of detection and false alarm rate).

• Journal of Korean Society of Forest Science
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• v.65 no.1
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• pp.54-59
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• 1984

Research was conducted in a minirotation plantation with four different planting densities at Tatae-ri, Chongwoon-myon, Yangpyong-gun, Kyonggi-do, to investigate the relation between volume and biomass production. Nine-year-old Alnus hirsuta var. sibirica analyzed to determine volume yield and weight equations for aboveground parts. The results suggest that the most suitable harvesting or thinning period at highly dense plots, more than 6,000 trees per hectare, is five years after planting, and the most fitted regression equation model for estimating aboveground biomass or total tree biomass is $logY=b_0+b_1logd^2h$.

• Korean Journal of Environment and Ecology
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• v.10 no.1
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• pp.49-57
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• 1996

To figure out the relation between the shade hours and the landscape tree growth in the apartment housing areas, the present sizes and planting positions of 4 tree species in Gwacheon-si apartment housing areas were surveyed. Then, shade hours were analyzed and the data were analyzed by simple linear regression method. As a whole, the R$^{2}$ was too low to generalize the regression equation. Therefore, it was presumed that the gravity of shade hours in landscape tree growth in this sample site was relatively lower than that of any other environmental factors. However, it was presumed that the characteristics of shade intolerant and tolerant tree were turned up, because Pinus strobus showed a low negative correlation with shade housm and Acer palmatum and Magnolia denudata showed a low positive correlation with shade hours generally. And, it was proved that the statistically significant cases were the tree diameter at root collar and tree sidth of Acer palmatum and tree width of Magnolia denudata with shade hours showing a low correlation coefficient less than 0.4.