• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1465-1475
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• 2013

The chi-square type test statistic is the most commonly used test in terms of measuring testing goodness-of-fit for multinomial logistic regression model, which has its grouped data (binomial data) and ungrouped (binary) data classified by a covariate pattern. Chi-square type statistic is not a satisfactory gauge, however, because the ungrouped Pearson chi-square statistic does not adhere well to the chi-square statistic and the ungrouped Pearson chi-square statistic is also not a satisfactory form of measurement in itself. Currently, goodness-of-fit in the ordinal setting is often assessed using the Pearson chi-square statistic and deviance tests. These tests involve creating a contingency table in which rows consist of all possible cross-classifications of the model covariates, and columns consist of the levels of the ordinal response. I examined goodness-of-fit tests for a proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. Using a simulation study, I investigated the distribution and power properties of this test and compared these with those of three other goodness-of-fit tests. The new test had lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. I illustrated the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.209-218
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• 2012

The method of generalized estimating equations (GEEs) provides consistent esti- mates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). In this paper we compare the estimators of parameters in GEE approach. We consider two aspects: coverage probabilites and efficiency. We adopted to ordinal responses th results derived from binary outcomes.

• Korean Journal of Exercise Nutrition
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• v.25 no.1
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• pp.30-34
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• 2021

[Purpose] Many studies have observed a high prevalence of erectile dysfunction among individuals performing physical activity in less leisure-time. However, this relationship in patients with type 2 diabetic patients is not well studied. In exposure outcome studies with ordinal outcome variables, investigators often try to make the outcome variable dichotomous and lose information by collapsing categories. Several statistical models have been developed to make full use of all information in ordinal response data, but they have not been widely used in public health research. In this paper, we discuss the application of two statistical models to determine the association of physical inactivity with erectile dysfunction among patients with type 2 diabetes. [Methods] A total of 204 married men aged 20-60 years with a diagnosis of type 2 diabetes at the outpatient unit of the Department of Endocrinology at PSG hospitals during the months of May and June 2019 were studied. We examined the association between physical inactivity and erectile dysfunction using proportional odds ordinal logistic regression models and continuation ratio models. [Results] The proportional odds model revealed that patients with diabetes who perform leisure time physical activity for over 40 minutes per day have reduced odds of erectile dysfunction (odds ratio=0.38) across the severity categories of erectile dysfunction after adjusting for age and duration of diabetes. [Conclusion] The present study suggests that physical inactivity has a negative impact on erectile function. We observed that the simple logistic regression model had only 75% efficiency compared to the proportional odds model used here; hence, more valid estimates were obtained here.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.25-46
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• 2020

This paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.

• Korean Journal of Organic Agriculture
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• v.20 no.2
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• pp.125-142
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• 2012

The purposes of this study are to analyze the effect of consumption of environment-friendly agricultural products (EFAP) on improvement of family health, and to suggest directions for improvement of family health. A survey was conducted for qualitative analysis regarding relationship between EFAP consumption and family health. The method of his study was employed Cross-tabulation and an Ordinal Logistic Regression Model to derive more significant results in analyzing factors of improvement of family health. The result shows that improvement of health has a significant positive relationship with consumption of EFAP. In addition, those consumers with high reliability and quality contentment are more likely to experience improvement of health. As consumers constantly eat EFAP, they are more likely to experience improvement of health. In order to provide consumer reliability of EFAP, more strict certification management system with sound monitoring and an appropriate penalty for violation should be established.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.91-102
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• 2020

The study investigates the relationship between the inflation rate and economic growth to find out the optimal inflation threshold for economic growth. Therefore, this study applied an ordinary least square model (OLS) and the ordinal regression model, and collected the time-series data from 1996 to 2017 to test the relationship between inflation and economic growth in the short-term and long-term. The sample fits the model and is statistically significant. The study showed that 96.6% of correlation between inflation rate and economic growth are close and 4.5% of optimal inflation threshold is appropriate for economic growth. It finds that the optimal inflation threshold is base to perform economic growth, besides the inflation rate is positively related to economic growth. The results support the monetary policy appropriately. This study identifies issues for Government to consider: have a comprehensive solution among macroeconomic policies, monetary policy, fiscal policy and other policies to control and maintain the inflation and stimulate growth; have appropriate policies to regulate inflation to stimulate economic growth over the long term; set a priority goal for sustainable economic growth; not pursue economic growth by maintaining the inflation rate in the long term, but take appropriate measures to stabilize the inflation at the optimal inflation threshold.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.313-322
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• 2005

In this paper, we discuss suppression for logistic regression model. Suppression for linear regression model was defined as the relationship among sums of squared for regression as well as correlation coefficients of. variables. Since it is not common to obtain simple correlation coefficient for binary response variable of logistic model, we consider cumulative logistic models with multinomial and ordinal response variables rather than usual logistic model. As number of category of a response variable for the cumulative logistic model gets collapsed into binary, it is found that suppressions for these logistic models are changed. These suppression results for cumulative logistic models are discussed and compared with those of linear model.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.203-223
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• 2022

We propose a Bayesian approach to cumulative logistic regression model for the ordinal response based on the orthogonal principal components via singular value decomposition considering the multicollinearity among predictors. The advantage of the suggested method is considering dimension reduction and parameter estimation simultaneously. To evaluate the performance of the proposed model we conduct a simulation study with considering a high-dimensional and highly correlated explanatory matrix. Also, we fit the suggested method to a real data concerning sprout- and scab-damaged kernels of wheat and compare it to EM based proportional-odds logistic regression model. Compared to EM based methods, we argue that the proposed model works better for the highly correlated high-dimensional data with providing parameter estimates and provides good predictions.

• Journal of Korean Society for Quality Management
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• v.32 no.3
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• pp.109-125
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• 2004

This paper deals with an optimal process control problem in production of hull structural steel plate with high defective rate. The main quality characteristic(dependent variable) is the internal quality(defect) of plates and is dependent on process parameters(independent variables). The dependent variable(quality characteristics) has three categorical ordinal data and there are 35 independent variables(29 continuous variables and 6 categorical variables). In this paper, we determine the main factors and to develop the mathematical model between internal quality predicted probabilities and the main factors. Secondly, we find out the optimal process condition of main factors through analysis of variance(ANOVA) using simulation. We consider three models to obtain the main factors and the optimal process condition: linear, quadratic, error models.

• Korean Journal of Organic Agriculture
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• v.20 no.1
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• pp.1-19
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• 2012

The purpose of this study is to analyze consumption of homemade organically processed food (HOPF), and to derive directions for consumption promotion of HOPF. A survey was conducted for quantitative analysis regarding consumption. This study used an Ordinal Logistic Regression Model to derive more significant results in analyzing factors of consumption. The findings was that younger consumers with high income are more likely to purchase HOPF. And those consumers with high price and quality contentment are more likely to purchase HOPF. And contentment with certification institutions and improvement of health have a significant positive relationship with consumption. Consumers were found to pay 51 percent more for HOPF than for non-HOPF products. This level show that the current level of price premium for HOPF is 51 percent higher than their desired level. In order to reduce the price premium for HOPF, effective policy programs should be developed. A targeted market strategy to sell HOPF to younger consumers with high income is needed to boost consumption. A strict certification management system should be established to enhance consumer reliability in HOPF.