• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1231-1246
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• 2016

Cause-specific hazard model (Prentice et al., 1978) and subdistribution hazard model (Fine and Gray, 1999) are mostly used for the right censored survival data with competing risks. Some other models for survival data with competing risks have been subsequently introduced; however, those models have not been popularly used because the models cannot provide reliable statistical estimation methods or those are overly difficult to compute. We introduce simple and reliable competing risk regression models which have been recently proposed as well as compare their methodologies. We show how to use SAS and R for the data with competing risks. In addition, we analyze survival data with two competing risks using five different models.

• Journal of dental hygiene science
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• v.8 no.3
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• pp.169-173
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• 2008

As a result of analyzing the effects of removing dental plaque according to using proxabrush by using the proportional odds models, targeting patients of practicing oral prophylaxis in juniors for the Department of Dental Hygiene at S university from March 10, 2007 to June 3, 2007, the following conclusions were obtained. 1. The goodness-of-fit in the proportional odds models is 1.2552 whose degree of freedom is 3, and p value is .7398, thereby implying that the proportional odds models are appropriate. And, regarding the effects of removing dental plaque and the independent matter of using proxabrush, as the test on $H_0:{\beta}=0$, the test statistics is 15.5496 whose degree of freedom is 1, and p value is 15.5496. This implies that there is high correlation between the effect of removing dental plaque and the use of proxabrush. 2. ML estimate on $\beta$ in the model can be $\hat{\beta}=1.2493$ (ASE = 0.3207). And, as for the tendency that the response will belong to being very good(this can be expressed to be $Y{\leq}j$) rather than being very bad, the tendency of using proxabrush is higher by the estimated odds ratio exp(1.2493) = 3.49 times than the response of not using proxabrush. 3. As for the estimated response in the proportional odds models, the estimated(cumulative) probability, which the response of using proxabrush is very good and will belong to the good effect of removing dental plaque, is 0.38(0.50).

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.889-898
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• 2005

In this paper we propose a new estimator of relative odds ratio in the two-sample case of proportional odds model under censorship. Also, we show that the estimator consistent and asymptotically normal by using martingale-representation. The efficiency of the proposed is assessed through a simulation study.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.327-334
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• 2005

In this paper we propose a new estimator of relative odds ratio in the two-sample case of proportional odds model. Also, we show that the estimator is consistent and asymptotically normal. The efficiency of the proposed is assessed through a simulation study.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1067-1076
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• 2006

Although the proportional hazards model is the most common approach used for studying the relationship of event times and covariates, alternative models are needed for occasions when it does not fit data. In the two-sample case, proportional odds models are useful for fitting data whose hazard rates converge asymptotically. In this thesis, we propose a new estimator of the relative odds ratio of the proportional odds model when two independent random samples are observed under uncensorship. We prove the asymptotic normality and consistency of the estimator by using martingale-representation. The efficiency of the proposed is assessed through a simulation study.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.845-854
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• 2016

Sentiment analysis or opinion mining is a technique of text mining employed to identify subjective information or opinions of an individual from documents in blogs, reviews, articles, or social networks. In the literature, only a problem of binary classification of ratings based on review texts in an online review. However, because there can be positive or negative reviews as well as neutral reviews, a multi-class classification will be more appropriate than the binary classification. To this end, we consider the multi-class classification of ratings based on review texts. In the preprocessing stage, we extract words related with ratings using chi-square statistic. Then the extracted words are used as input variables to multi-class classifiers such as support vector machines and proportional odds model to compare their predictive performances.