• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.775-791
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• 2017

We frequently encounter binary data in real life. Logistic, Probit, Cauchit, Complementary log-log models are often used for binary data analysis. In order to analyze binary data, Liu (2004) proposed a Robit model, in which the inverse of cdf of the Student's t distribution is used as a link function. Kim et al. (2008) also proposed a generalized t-link model to make the binary regression model more flexible. The more flexible skewed distributions allow more flexible link functions in generalized linear models. In the sense, we propose a binary data regression model using skewed generalized t distributions introduced in Theodossiou (1998). We implement R code of the proposed models using the glm function included in R base and R sgt package. We also analyze Pima Indian data using the proposed model in R.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1305-1315
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• 2015

Consider the risk score which is a function of a linear score for the classification models. The AUC optimization method can be applied to estimate the coefficients of linear score. These estimates obtained by this AUC approach method are shown to be better than the maximum likelihood estimators using logistic models under the general situation which does not fit the logistic assumptions. In this work, the VUS and HUM approach methods are suggested by extending AUC approach method for more realistic discrimination and prediction worlds. Some simulation results are obtained with both various distributions of thresholds and three kinds of link functions such as logit, complementary log-log and modified logit functions. It is found that coefficient prediction results by using the VUS and HUM approach methods for multiple categorical classification are equivalent to or better than those by using logistic models with some link functions.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.503-511
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• 2010

The goal of this paper is to suggest an algorithm to get the number of student on the elementary, meddle and high-school for the forecasting of the numbers of student by the moving average method using a proportional expression. Comparing with the results of Korean education statistical system 2005, 2006, and 2007, the results of this paper are better than those of the Korean education statistical system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.242-249
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• 2004

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for the practical purpose of forecasting forest fire danger. Forest fire in South Korea is highly influenced by humidity, wind speed, and temperature. To effectively forecast forest fire occurrence, we need to develop a forest fire danger rating model using weather factors associated with forest fire. Forest fore occurrence patterns were investigated statistically to develop a forest fire danger rating index using time series weather data sets collected from 8 meteorological observation centers. The data sets were for 5 years from 1997 through 2001. Development of the forest fire occurrence probability model used a logistic regression function with forest fire occurrence data and meteorological variables. An eight-province probability model by was developed. The meteorological variables that emerged as affective to forest fire occurrence are effective humidity, wind speed, and temperature. A forest fire occurrence danger rating index of through 10 was developed as a function of daily weather index (DWI).

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.393-399
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• 1993

When we compute the maximum likelihood estimators of the parameters for the logistic regression models, which are useful in studying the relationship between the binary response variable and the explanatory variable, the standard error calculations are usually based on the second derivative of log-likelihood function. On the other hand, an estimator of the Fisher information motivated from the fact that the expectation of the cross-product of the first derivative of the log-likelihood function gives the Fisher information is expected to have similar asymptotic properties. These estimators of Fisher information are closely related with the iterative algorithm to get the maximum likelihood estimator. The average numbers of iterations to achieve the maximum likelihood estimator are compared to find out which method is more efficient, and the estimators of the variance from each method are compared as estimators of the asymptotic variance.

• Journal of Preventive Medicine and Public Health
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• v.54 no.6
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• pp.471-480
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• 2021

Objectives: Cadmium is widely used, leading to extensive environmental and occupational exposure. Unlike other organs, for which the harmful and carcinogenic effects of cadmium have been established, the hepatotoxicity of cadmium remains unclear. Some studies detected correlations between cadmium exposure and hepatotoxicity, but others concluded that they were not associated. Thus, we investigated the relationship between cadmium and liver damage in the general population. Methods: In total, 11 838 adult participants from National Health and Nutrition Examination Survey 1999-2015 were included. Urinary cadmium levels and the following liver function parameters were measured: alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), total bilirubin (TB), and alkaline phosphatase (ALP). Linear and logistic regression analyses were performed to assess the associations between urinary cadmium concentrations and each liver function parameter after adjusting for age, sex, race/ethnicity, annual family income, smoking status, alcohol consumption status, physical activity, and body mass index. Results: The covariate-adjusted results of the linear regression analyses showed significant positive relationships between log-transformed urinary cadmium levels and each log-transformed liver function parameter, where beta±standard error of ALT, AST, GGT, TB, and ALP were 0.049±0.008 (p<0.001), 0.030±0.006 (p<0.001), 0.093±0.011 (p<0.001), 0.034±0.009 (p<0.001), and 0.040±0.005 (p<0.001), respectively. Logistic regression also revealed statistically significant results. The odds ratios (95% confidence intervals) of elevated ALT, AST, GGT, TB, and ALP per unit increase in log-transformed urinary cadmium concentration were 1.360 (1.210 to 1.528), 1.307 (1.149 to 1.486), 1.520 (1.357 to 1.704), 1.201 (1.003 to 1.438), and 1.568 (1.277 to 1.926), respectively. Conclusions: Chronic exposure to cadmium showed positive associations with liver damage.

• Journal of Sasang Constitutional Medicine
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• v.36 no.1
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• pp.13-30
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• 2024

Objectives This study aimed to analyze the influence of various sleep-related factors that affect sleep quality by each Sasang type. Methods A total of 400 subjects were included for this study, 108 males and 292 females. Sasang type was diagnosed using the SCAT. Then, the characteristics of each Sasang type were analyzed using HRV, DITI, and PSQI, PSS, and SDFI questionnaires. Logistic regression analysis was used to predict sleep-related factors that affect sleep disorders by Sasang types. Results This study shows that the pathophysiological characteristics for stress and digestive function of each Sasang type can differentiate sleep management through a logistic regression model including subscales of PSS and SDFI. Stress had no effect on the occurrence of sleep disturbance within only So-Eum, since the stress level is originally high in the So-Eum regardless of sleep quality. Rather, decreased appetite and poor eating habits had a significant impact on the decline in sleep quality. In addition, poor digestion and eating habits in So-Yang had a greater impact and poor digestion in Tae-Eum had a greater impact on the decline in sleep quality. Conclusion The stress and subscales of digestive function provide differentiated sleep management in So-Yang, Tae-Eum, and So-Eum types. The individualized sleep management for each Sasang type with statistically validated PSS and SDFI would be useful for sleep-related experts planning safe and effective person-centered health care as well as for Western clinicians who want to incorporate Sasang typology into their treatments as integrative medical technique in the future.

• Korean Journal of Clinical Pharmacy
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• v.34 no.2
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• pp.118-125
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• 2024

Background: Numerous studies have consistently demonstrated that depression can be associated with cognitive function decline, primarily focusing on older adults due to the neurodegenerative characteristics of dementia. With persistent depression frequently reported in patients with early-onset or young-onset dementia, this study aimed to assess the impact of depression, specifically the changes in depressive symptoms over time, on the risk of cognitive function decline in middle-aged adults in Korea. Methods: This retrospective study utilized data from the first four waves (2006-2012) of the Korean Longitudinal Study of Aging (KLoSA), focusing on middle-aged adults with normal cognitive function at baseline. Changes in depressive symptoms were categorized into four groups based on the CES-D score, and their association with cognitive function decline was evaluated using a multivariate logistic regression model. Results: Of the initial 10,254 participants, 3,400 were included in the analysis. Depressive status, particularly newly onset (adjusted odds ratio [aOR] 1.96; 95% confidence interval [CI] 1.32-2.93) and persistent depression groups (aOR 5.59; 95% CI 2.90-10.78), were significantly associated with cognitive function decline. In contrast, recovery from depressive symptoms was not significantly associated with cognitive function decline (p=0.809). Conclusions: Our study showed a significant association between changes in depressive symptoms and cognitive function decline in middle-aged Korean adults. This suggests that management of depressive symptoms could be crucial for the prevention of cognitive function decline in this population.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.931-940
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• 2011

Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and PCA based discriminant analysis by some real and artificial data. It is concluded that the new method has better power as compared with other methods.

• Journal of Korean Society of Forest Science
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• v.99 no.2
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• pp.163-171
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• 2010

Forest fires have potentials to change the structure and function of forest ecosystems and significantly influence on atmosphere and biogeochemical cycles. Forest fire also affects the quality of public benefits such as carbon sequestration, soil fertility, grazing value, biodiversity, or tourism. The prediction of fire occurrence and its spread is critical to the forest managers for allocating resources and developing the forest fire danger rating system. Most of fires were human-caused fires in Korea, but meteorological factors are also big contributors to fire behaviors and its spread. Thus, meteorological factors as well as social factors were considered in the fire danger rating systems. A total of 298 forest fires occurred during the fall season from 2002 to 2006 in South Korea were considered for developing a logistic model of forest fire occurrence. The results of statistical analysis show that only effective humidity and temperature significantly affected the logistic models (p<0.05). The results of ROC curve analysis showed that the probability of randomly selected fires ranges from 0.739 to 0.876, which represent a relatively high accuracy of the developed model. These findings would be necessary for the policy makers in South Korea for the prevention of forest fires.