• 동아시아식생활학회지
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• 제16권4호
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• pp.488-495
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• 2006

The purposes of this study were to measure respondent's demographic characteristics, respondent's attitudes toward chicken meat, and factors influencing the level of perceived helpfulness of country of origin in predicting the safety of chicken. The data was collected through a consumer survey during the March 2006. Two hundred fifty meat consumers living in Suncheon, the eastern part of Chonnam, were randomly selected as respondents. Eleven respondents did not complete the survey instrument, resulting in a final sample size of 239. All estimations were carried out using correlation, logistic procedure of SAS package, and plum procedure of SPSS. The level of perceived helpfulness of country of origin in predicting the safety of chicken meat was significantly correlated with trust, antibiotics and salmonella/bacteria among the attitude variables. The proportional odds assumption of the model was violated at p<0.05. The estimated results of the multinomial logit model indicated that income, single, occupation, and education significantly affected helpful perception over not helpful perception, while gender and occupation significantly affected very helpful perception over not helpful perception in the case of the extended model. These study results from this study could be useful in developing marketing and health promotion strategies, as well as government trade policy.

• 한국학교ㆍ지역보건교육학회지
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• 제23권4호
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• pp.81-93
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• 2022

Objectives: The purpose of this study was to develop a model for comprehensively evaluating the risk of sarcopenia in Korean adults and to generate the sarcopenia risk scorecard model based on the results. Methods: The participants of the study were 7,118 adults without sarcopenia in the first basic survey, and a longitudinal analysis was conducted using data from the 1st to 8th survey (2006-2020) of the Korean Longitudinal Study of Aging (KLoSA). The data were analyzed using Rao-Scott chi-square test and weighted Cox proportional hazards regression of complex sampling design. The sarcopenia risk scorecard model was developed by Cox proportional hazards regression using points to double the odds (PDO) method. Results: The findings show that the risk factors for sarcopenia in Korean adults were gender, age, marital status, socioeconomic status, body mass index (BMI), regular exercise, diabetes and arthritis diagnosis. In the scorecard results, the case of exposure to the highest risk level was 100 points. The highest score range were given in the order of age over 65, low BMI, and low socioeconomic status. Conclusions: The significance of this study is that the causal relationship between various factors and the occurrence of sarcopenia in Korean adults was identified. Also, the model developed in this study is expected to be useful in detecting participants with risk of sarcopenia in the community early and preventing and managing sarcopenia through appropriate health education.

• Journal of the Korean Data and Information Science Society
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• 제27권4호
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• pp.1117-1117
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• 2016

Journal of the Korean Data & Information Science Society, Vol. 24, No. 6, 1465-1475, 2013 (http://dx.doi.org/10.7465/jkdi.2013.24.6.1465). This article has been retracted at the research ethics committee.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.605-625
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• 2017

This paper presents proportional odds cure models to allow spatial correlations by including spatial frailty in the interval censored data setting. Parametric cure rate models with independent and dependent spatial frailties are proposed and compared. Our approach enables different underlying activation mechanisms that lead to the event of interest; in addition, the number of competing causes which may be responsible for the occurrence of the event of interest follows a Geometric distribution. Markov chain Monte Carlo method is used in a Bayesian framework for inferential purposes. For model comparison some Bayesian criteria were used. An influence diagnostic analysis was conducted to detect possible influential or extreme observations that may cause distortions on the results of the analysis. Finally, the proposed models are applied for the analysis of a real data set on smoking cessation. The results of the application show that the parametric cure model with frailties under the first activation scheme has better findings.

• 한국식생활문화학회지
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• 제21권5호
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• pp.439-445
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• 2006

The purpose of this study was to measure respondent's demographic characteristics, respondent's attitudes toward chicken, and factor influencing on the level of perceived helpfulness of country of origin in predicting the quality of chicken. The data was collected through a consumer survey during the March 2006. A total number of 250 meat consumers living in Suncheon, the eastern part of Chonnam, were randomly selected as respondents. Eleven respondents did not complete the survey instrument, resulting in a final sample size of 239. All estimations were carried out using chi-square, correlation, and logistic procedure of SAS package. The results are as follows. The level of perceived helpfulness of country of origin in predicting the quality of chicken was significantly different by age and occupation of demographic variables, and was significantly correlated with respondent informed of attitude variables. The proportional odds assumption of model was not violated at p<0.05. The effects of income, occupation and respondent informed on the level of perceived helpfulness of country of origin in predicting the quality of chicken. The results from this study could be useful in developing marketing and health promotion strategies, as well as government trade policy.

• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.735-749
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• 2008

This article suggests application of logit model for analysis of multicategory responses. Referring to the reference category, characteristic of each category is obtained from analysis of polytomous logit model. With National Pension data it is illustrated that application of logit model helps it possible to find significant factors which may not be found only with polytomous logit model. Application of the logit model is done by reducing the number of categories. Categories are grouped into the former and the latter group according to reference category. Extra finding of significant factor was possible from logistic regression analysis for the two groups after removing the reference category. It is expected that this application would be helpful for finding information and characteristics on ordered multicategory responses where the proportional odds model does not fit.

• Journal of the Korean Data and Information Science Society
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• 제27권4호
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• pp.845-854
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• 2016

감성분석 (sentiment analysis) 혹은 오피니언 마이닝 (opinion mining)은 블로그, 리뷰, 신문기사나 소셜네트워크 등의 문서에서 개인의 주관적인 정보 혹은 의견을 알아보는데 사용되는 텍스트 마이닝의 기법이다. 평점이 있는 온라인 리뷰에서 리뷰 텍스트에 기반한 평점의 분류문제에 대한 선행연구에서는 이진 분류만을 고려하였다. 그러나 긍정과 부정 외에도 중립적인 의견도 있을 수 있기 때문에 이진 분류보다는 다범주 분류가 더 적합할 것이다. 본 연구에서는 리뷰 텍스트에 기반한 평점의 다범주 분류문제를 고려한다. 전처리에서는 카이제곱 통계량을 이용하여 평점과 연관된 단어들을 추출하고 이를 입력변수로 삼아 지지벡터기계 (support vector machines)와 비례오즈 모형 (proportional odds model) 등 다범주 분류기의 예측력을 비교한다.

• Communications for Statistical Applications and Methods
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• 제29권2호
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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.

• 보건행정학회지
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• 제14권2호
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• pp.1-16
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• 2004

The study purpose was to find which factors affect selection of hospital network types. This study used the 1998 American Hospital Association Annual Survey Database from Health Forum. Among these U.S. hospitals, the researcher selected hospitals located in Metropolitan Statistical Areas. Therefore the final observation cases for analysis are 1,971 Metropolitan Statistical Area hospitals in the United States. To identify significant variables influencing hospital network types, the study used proportional odds logistics regression model on population size, Health Maintenance Organization penetration rate, and market competition rate of area including a hospital, types of hospital ownership, hospital bed size, proportion of Medicare patients and Medicaid patients in total hospital patients, and occupancy rate. Contrary to conventional wisdom, selection of hospital network types was influenced by population size of area which a hospital located, types of ownership, hospital bed size, and proportion of medicare patients rather than Health Maintenance Organization penetration. Population size 1,000,000-2,499,999 had the highest probability of selecting type IV (clinical-vertical integration) from an independent hospital, and a religious group owned hospitals and for-profit owned hospitals had the highest probability of selecting Type IV (clinical-vertical integration) from an independent hospital. A bed size had positive relation on selecting Type IV (clinical-vertical integration) from an independent hospital. Unlikely general belief that the selecting types of hospital network was determined by the change of health insurance policy such as Health Maintenance Organizations and Preferred Provider Organizations, the types of hospital network were influenced by community characteristics such as population size, and hospital characteristics.

• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.697-705
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• 2009

The Pearson chi-squared statistic or the deviance statistic is widely used in assessing the goodness-of-fit of the generalized linear models. But these statistics are not proper in the situation of continuous explanatory variables which results in the sparseness of cell frequencies. We propose a goodness-of-fit test statistic for the cumulative logit models with ordinal responses. We consider the grouping of a dataset based on the ordinal scores obtained by fitting the assumed model. We propose the Pearson chi-squared type test statistic, which is obtained from the cross-classified table formed by the subgroups of ordinal scores and the response categories. Because the limiting distribution of the chi-squared type statistic is intractable we suggest the parametric bootstrap testing procedure to approximate the distribution of the proposed test statistic.