• Journal of the Korean Statistical Society
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• 제26권4호
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• pp.493-505
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• 1997

The analysis of binary data appears to many areas such as statistics, biometrics and econometrics. In many cases, data are often collected in which some observations are incomplete. Assume that the missing covariates are missing at random and the responses are completely observed. A method to Bayesian analysis of the binary regression model with incomplete data is presented. In particular, the desired marginal posterior moments of regression parameter are obtained using Meterpolis algorithm (Metropolis et al. 1953) within Gibbs sampler (Gelfand and Smith, 1990). Also, we compare logit model with probit model using Bayes factor which is approximated by importance sampling method. One example is presented.

• Communications for Statistical Applications and Methods
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• 제13권2호
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• pp.369-378
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• 2006

This Study focuses on the binary forecast of occurrence of heavy snow in Honam area based on the MOS(model output statistic) method. For our study daily amount of snow cover at 17 stations during the cold season (November to March) in 2001 to 2005 and Corresponding 45 RDAPS outputs are used. Logistic regression model and neural networks are applied to predict the probability of occurrence of Heavy snow. Based on the distribution of estimated probabilities, optimal thresholds are determined via true shill score. According to the results of comparison the logistic regression model is recommended.

• Journal of the Korean Statistical Society
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• 제29권4호
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• pp.407-422
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• 2000

This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

• 응용통계연구
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• 제30권5호
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• pp.775-791
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• 2017

이진 데이터는 일상 생활에서 자주 접할 수 있는 데이터이다. 이진 데이터를 회귀 분석하는 방법으로 로지스틱(Logistic), 프로빗(Probit), Cauchit, Complementary log-log 모형이 주로 쓰이는데, 이 방법 이외에도 Liu(2004)가 제시한 t 분포를 이용한 로빗(Robit) 모형, Kim 등 (2008)에서 제시한 일반화 t-link 모형을 이용한 방법 등이 있다. 유연한 분포를 이용하면 유연한 회귀 모형이 가능해지는 점에 착안하여, 이 논문에서는 Theodossiou(1998)에서 제시된 기운 일반화 t 분포 (Skewed Generalized t Distribution)의 이용하여 우도 함수를 최대로 하는 이진 데이터 회귀 모형을 소개한다. 기운 일반화 t 분포를 R glm 함수, R sgt 패키지를 연결하여 이 논문에서 제시한 방법을 R로 분석할 수 있는 방법을 소개하고, 피마 인디언(Pima Indian) 데이터를 분석한다.

• Journal of the Korean Statistical Society
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• 제28권2호
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• pp.167-182
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• 1999

This article is concerned with the selection of subsets of predictor variables to be included in building the binary response probit regression model. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the probit regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. The appropriate posterior probability of each subset of predictor variables is obtained through the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as the one with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• Communications for Statistical Applications and Methods
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• 제29권3호
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• pp.319-331
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• 2022

In this paper, we develop a new statistical model to forecast the PM_2.5 level in Seoul, South Korea. The proposed model is based on the extreme quantile regression model with lasso penalty. Various meteorological variables and air pollution variables are considered as predictors in the regression model, and the lasso quantile regression performs variable selection and solves the multicollinearity problem. The final prediction model is obtained by combining various extreme lasso quantile regression estimators and we construct a binary classifier based on the model. Prediction performance is evaluated through the statistical measures of the performance of a binary classification test. We observe that the proposed method works better compared to the other classification methods, and predicts 'very bad' cases of the PM_2.5 level well.

• Journal of the Korean Data and Information Science Society
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• 제25권2호
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• pp.403-410
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• 2014

이분형 로지스틱 회귀분석은 양적 혹은 질적 설명변수를 이용해서 이분형 반응변수를 설명하는 하나의 통계적인 기법이다. 이 모형에서는 반응변수가 1이 될 확률을 설명변수들의 선형결합의 변환(혹은 함수)으로 설명하고자 한다. 이 개념에 대한 이해가 비통계학자들이 이분형 로지스틱 회귀모형을 이해하는데 있어서 넘어야 할 커다란 장벽 중의 하나이다. 이 연구에서는 이분형 로지스틱 회귀모형의 필요성을 엑셀 VBA를 이용하여 설명하는 교육도구를 개발하고자 한다. 반응변수가 1이 될 확률을 설명변수의 선형함수로 모형화 할 때의 문제점과 선형결합에 대한 변환을 통해 이 문제점이 어떻게 해소되는지 보여준다.

• 대한산업공학회지
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• 제40권3호
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• pp.342-357
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• 2014

The present study analyzes the relationship between research and development (R&D) inputs and performance of a national technology innovation R&D program using successive binary Logistic regression models based on a typical R&D logic model. In particular, this study focuses on to answer the following three main questions; (1) "To what extent, do the R&D inputs have an effect on the performance creation?"; (2) "Is an obvious relationship verified between the immediate predecessor and its successor performance?"; and (3) "Is there a difference in the performance creation between R&D government subsidy recipient types and between R&D collaboration types?" Methodologically, binary Logistic regression models are established successively considering the "Success-Failure" binary data characteristic regarding the performance creation. An empirical analysis is presented analyzing the sample n = 2,178 R&D projects completed. This study's major findings are as follows. First, the R&D inputs have a statistically significant relationship only with the short-term, technical output, "Patent Registration." Second, strong dependencies are identified between the immediate predecessor and its successor performance. Third, the success probability of the performance creation is statistically significantly different between the R&D types aforementioned. Specifically, compared with "Large Company", "Small and Medium-Sized Enterprise (SMS)" shows a greater success probability of "Sales" and "New Employment." Meanwhile, "R&D Collaboration" achieves a larger success probability of "Patent Registration" and "Sales."

• 응용통계연구
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• 제33권1호
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• pp.47-59
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• 2020

본 논문에서는 마코프 이항 회귀 모형의 시차가 알려져 있거나 그렇지 않은 경우일 때, t-링크 함수를 갖는 종단적 마코프 이항 회귀 모형을 제시한다. 일반적으로, 이항 회귀 모형에서는 로직 모형이나 프로빗 모형이 주로 사용된다. t-링크 함수는 t 분포가 자유도가 커질수록 정규분포로 근사하기 때문에 프로빗 모형을 대신 더 많은 유연성을 위해 사용될 수 있다. 게다가 마코프 회귀모형은 종단 자료에 대해 사용될 수 있다. 우리는 마코프 회귀 모형의 시차를 결정하기 위해 베이지안 방법을 제시하고자 한다. 특히, 각 모델의 차수에 대해 알고 있는 경우에는 DIC를 기준으로 모델 비교를 실시하였다. 모델의 차수에 대해 모르는 경우에는 가능한 모델들의 사후 확률을 이용하였다. 복잡한 베이지안 계산을 해결하기 위하여 Albert와 Chib (1993), Kuo와 Mallick (1998)과 Erkanli 등 (2001)의 방법을 이용하여 모델을 재설정하였다. 제안하는 방법은 시뮬레이션 데이터와 Somer 등 (1984)에 의해 조사된 인도네시아 어린이 종단 데이터에 적용했다. 마코프 이항 회귀모형의 순서에 대해서 아는 경우와 모르는 경우를 각각 가정하여 최적의 모델을 알아보기 위해 MCMC 방법을 사용하였다. 또한, 매트로폴리스 해스팅 알고리즘의 수렴성을 점검하기 위해 Gelman과 Rubin의 진단을 이용했다.

• Communications for Statistical Applications and Methods
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• 제17권1호
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• pp.117-126
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• 2010

Clinical researchers often analyze survival data as binary outcomes using the logistic regression method. This paper examines the information loss resulting from analyzing survival time as binary outcomes. We first demonstrate that, under the proportional hazard assumption, this binary discretization does result in a significant information loss. Second, when fitting a logistic model to survival time data, researchers inadvertently use the maximal statistic. We implement a numerical study to examine the properties of the reference distribution for this statistic, finally, we show that the logistic regression method can still be a useful tool for analyzing survival data in particular when the proportional hazard assumption is questionable.