• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1513-1521
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• 2015

Selecting relevant variables for a statistical model is very important in regression analysis. Recently, variable selection methods using a penalized likelihood have been widely studied in various regression models. The main advantage of these methods is that they select important variables and estimate the regression coefficients of the covariates, simultaneously. In this paper, we propose a simple procedure based on a penalized h-likelihood (HL) for variable selection in Poisson hierarchical generalized linear models (HGLMs) for correlated count data. For this we consider three penalty functions (LASSO, SCAD and HL), and derive the corresponding variable-selection procedures. The proposed method is illustrated using a practical example.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.589-597
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• 2021

In simple and multiple regression, there is a difference in the meaning of regression coefficients, and not only are the estimates of regression coefficients different, but they also have different signs. Understanding the relative contribution of explanatory variables in a regression model is an important part of regression analysis. In a standardized regression model, the regression coefficient can be interpreted as the change in the response variable with respect to the standard deviation when the explanatory variable increases by the standard deviation in a situation where the values of the explanatory variables other than the corresponding explanatory variable are fixed. However, the size of the standardized regression coefficient is not a proper measure of the relative importance of each explanatory variable. In this paper, the estimator of the regression coefficient in multiple regression is expressed as a function of the correlation coefficient and the coefficient of determination. Furthermore, it is considered in terms of the effect of an additional explanatory variable and additional increase in the coefficient of determination. We also explore the relationship between estimates of regression coefficients and correlation coefficients in various plots. These results are specifically applied when there are two explanatory variables.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.181-194
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• 2017

This study aims to develop a regression model for forest volume estimation using field-collected forest inventory information and airborne LiDAR data. The response variable of the model is forest stem volume, was measured by random sampling from each individual plot of the 30 circular sample plots collected in Bonghwa-gun, Gyeong sangbuk-do, while the predictor variables for the model are Height Percentiles(HP) and Height Bin(HB), which are metrics extracted from raw LiDAR data. In order to find the most appropriate model, the candidate models are constructed from simple linear regression, quadratic polynomial regression and multiple regression analysis and the cross-validation tests were conducted for verification purposes. As a result, $R^2$ of the multiple regression models of $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}$ among the estimated models was the highest at 0.509, and the PRESS statistic of the simple linear regression model of $HP_{25}$ was the lowest at 122.352. $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}-based$ models, thus, are comparatively considered more appropriate for Korean forests with complicated vertical structures.

• Journal of Korean Society on Water Environment
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• v.28 no.2
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• pp.224-231
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• 2012

It is necessary to develop flow duration curve (FDC) on each unit watershed in order to analyze flow conditions in the stream for the management of Total Maximum Daily Loads (TMDLs). This study investigated a simple method to develop FDC for the general use of the curve. A simple equation for daily flow estimation was derived from the regression analysis between the 8-day interval flow data of a unit watershed and the daily flow monitoring data of an adjacent upstream region. FDC can be prepared with the calculation of daily flow by the equation for each unit watershed. An annual and a full-period FDC were drawn for each unit watershed in Guem river basin. Standard flow such as low and ordinary flow can be obtained from the annual FDC. Major percentile of flow such as 10, 25, 50, 75 or 90% can be obtained from the full-period FDC. It is considered that this simple method of developing FDC can be utilized more widely for the calculation of standard flow and the assessment of water quality in the process of TMDLs.

• Earthquakes and Structures
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• v.19 no.3
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• pp.189-196
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• 2020

The near-collapse performance limit is defined as the deformation at the 20% drop of maximum base shear in the decreasing region of the pushover curve for ductile framed buildings. Although monotonic pushover analysis is preferred due to the simple application procedure, this analysis gives rise to overestimated results by neglecting the cumulative damage effects. In the present study, the acceptabilities of monotonic and cyclic pushover analysis results for the near-collapse performance limit state are determined by comparing with Incremental Dynamic Analysis (IDA) results for a 5-story Reinforced Concrete framed building. IDA is performed to obtain the collapse point, and the near-collapse drift ratios for monotonic and cyclic pushover analysis methods are obtained separately. These two alternative drift ratios are compared with the collapse drift ratio. The correlations of the maximum tensile and compression strain at the base columns and beam plastic rotations with interstory drift ratios are acquired using the nonlinear time history analysis results by the simple linear regression analyses. It is seen that these parameters are highly correlated with the interstory drift ratios, and the results reveal that the near-collapse point acquired by monotonic pushover analysis causes unacceptably high tensile and compression strains at the base columns, as well as large plastic rotations at the beams. However, it is shown that the results of cyclic pushover analysis are acceptable for the near-collapse performance limit state.

• Korean Journal of Child Studies
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• v.29 no.5
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• pp.215-226
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• 2008

The purpose of this study was to identify dangerous routes by which the experience of victimization leads to bullying behavior and to clarify the intrapersonal moderating variables which control the routes. Subjects were 1,086 students of elementary and middle schools in Seoul and Gyeonggi Province. Data were analyzed by simple regression analysis and multiple moderating regression analysis. The major findings of this study were that (1) the experience of victimization was an important factor predicting bullying behavior; and (2) self esteem and internal locus of control were moderating variables between the experience of victimization and bullying behavior. This study provides effective information to protect students from bullying by finding some moderating variables.

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.3
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• pp.313-322
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• 2000

The purpose of this study was to investigate the characteristic and image of oriental costume designs which represented among three countries, Korea, China and Japan. The specific objectives were: 1) to find out the positioning of oriental costume design. 2) to find out relation to oriental costume image and preference. The stimulus were 75 costume designs of contemporary costume which represented the traditional images of three countries Korea, China and Japan. The main survey of questionary consisted of their evaluation of the oriental costume image by 26 semantic differential bi-polar scales and the subjects were 99 female students majoring in clothing and textile. The data were analyzed by Multidimensional Scaling Method and Regression Analysis. The specific objective were as follows: 1. According to image positioning. The oriental costume design was classified by simple-decorative, soft-hard. 2. As result of regression analysis. The preference of oriental costume image was related to attractive factor.

• Asia pacific journal of information systems
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• v.15 no.2
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• pp.51-63
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• 2005

There has been substantial research into the bankruptcy prediction. Many researchers used the statistical method in the problem until the early 1980s. Since the late 1980s, Artificial Intelligence(AI) has been employed in bankruptcy prediction. And many studies have shown that artificial neural network(ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance, it has some problems such as overfitting and poor explanatory power. To overcome these limitations, this paper suggests a relatively new machine learning technique, support vector machine(SVM), to bankruptcy prediction. SVM is simple enough to be analyzed mathematically, and leads to high performances in practical applications. The objective of this paper is to examine the feasibility of SVM in bankruptcy prediction by comparing it with ANN, logistic regression, and multivariate discriminant analysis. The experimental results show that SVM provides a promising alternative to bankruptcy prediction.

• Structural Engineering and Mechanics
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• v.60 no.4
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• pp.655-665
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• 2016

This paper aims to develop models to accurately predict the behavior of fresh concrete exposed to vibration using artificial neural networks (ANNs) model and regression model (RM). For this purpose, behavior of a full scale precast concrete mold was investigated experimentally and numerically. Experiment was performed under vibration with the use of a computer-based data acquisition system. Transducers were used to measure time-dependent lateral displacements at some points on mold while both mold is empty and full of fresh concrete. Modeling of empty and full mold was made using both ANNs and RM. For the modeling of ANNs: Experimental data were divided randomly into two parts. One of them was used for training of the ANNs and the remaining part was used for testing the ANNs. For the modeling of RM: Sinusoidal regression model equation was determined and the predicted data was compared with measured data. Finally, both models were compared with each other. The comparisons of both models show that the measured and testing results are compatible. Regression analysis is a traditional method that can be used for modeling with simple methods. However, this study also showed that ANN modeling can be used as an alternative method for behavior of fresh concrete exposed to vibration in precast concrete structures.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1009-1018
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• 2013

Regression analysis is useful to understand the relationship of variables; however, we need to test if the slope of each regression lines is the same when comparing several populations. This paper suggests a new parallelism test for several linear regression lines. We use F-test of ANOVA and Kruskal-Wallis (1952) tests after obtaining slope estimator from a sequential slope. In addition, a Monte Carlo simulation study is adapted to compare the power of the proposed methods with those of Park and Kim (2009).