• Title/Summary/Keyword: Ordered Logistic Regression Model

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A Bayesian Threshold Model for Ordered Categorical Traits (순서범주형자료 분석을 위한 베이지안 분계점 모형)

  • Choi Byangsu;Lee Seung-Chun
    • The Korean Journal of Applied Statistics
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    • v.18 no.1
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    • pp.173-182
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    • 2005
  • A Bayesian threshold model is considered to analyze binary or ordered categorical traits. Gibbs sampler for making full Bayesian inferences about the category probability as well as the regression coefficients is described. The model can be regarded as an alternative to the ordered logit regression model. Numerical examples are shown to demonstrate the efficiency of the model.

Analysis of Residential Environment Satisfaction and Residential Preference in Daegu Downtown (대구 도심의 주거환경만족도와 거주의향 분석)

  • Song, Heung-Soo;Im, Jun-Hong;Kim, Han-Soo
    • Journal of the Korean housing association
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    • v.26 no.5
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    • pp.133-141
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    • 2015
  • As an empirical study on Daegu Downtown showing decentralization phenomenon, the purpose of this study is, based on the residential satisfaction research targeting the Downtown residents, to analyze the residential environment satisfaction and residential preference. Considering the parameters of measurement, we used the Ordered Logit Model and Logistic Regression. The results are as follows: First, the comprehensive residential environment satisfaction is relatively lower than that in 2008 and the residential preference in Downtown is similar to that of the past. Second, among the 7 factors that constitute the Downtown residential environment, the house, the landscape, and the security have a relatively large influence on the comprehensive residential environment satisfaction. Third, the residential environment factors which affect those who are hoping continuous Downtown residence are the safety, the house and the complex.

Analysis of online food purchasing behavior: a study of Sri Lankan consumers

  • Piyumi Wijesinghe;Shashika D. Rathnayaka;Niranga Bandara;Jung Min Heo;Dinesh D. Jayasena
    • Korean Journal of Agricultural Science
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    • v.50 no.4
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    • pp.927-940
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    • 2023
  • Online shopping has been undergoing significant developments in the South Asian region in the last decade. Using a representative sample of Sri Lankan consumers, this study explored online food purchasing behavior in Sri Lanka, a developing nation and island in South Asia. Data were collected from 562 respondents from all nine provinces in Sri Lanka using an online survey. Consumer attitudes were evaluated using factor analysis, and factor scores were added as explanatory variables to the final model. An ordered logistic regression model was used to examine the impact of consumer demographics, economic variables, and consumer attitudes on online food purchases. Online food purchasing intensity was categorized into four groups that suited ordinal rankings: zero for never, low for rarely, medium for occasionally, and high for regularly. Results indicated that age, income, education, and living in urban areas affect the online food purchasing behavior of Sri Lankan consumers. In addition, trust, convenience, and attitudes toward price were powerful drivers of online food purchasing. The findings have a number of significant managerial ramifications for creating strategies to promote online food purchases in developing South Asian nations like Sri Lanka. Moreover, promoting online shopping could be a potential solution for traffic congestion, ultimately helping to mitigate the negative externalities associated with it, such as carbon emissions and air pollution.

Analysis of Multicategory Responses with Logit Model on Earlyold Age Pension

  • Kim, Mi-Jung
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.3
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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.

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Imputation for Binary or Ordered Categorical Traits Based on the Bayesian Threshold Model (베이지안 분계점 모형에 의한 순서 범주형 변수의 대체)

  • Lee Seung-Chun
    • The Korean Journal of Applied Statistics
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    • v.18 no.3
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    • pp.597-606
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    • 2005
  • The nonresponse in sample survey causes a problem when it comes time to analyze dataset in public-use files where the user has only complete-data methods available and has limited information about the reasons for nonresponse. Recently imputation for nonresponse is becoming a standard approach for handling nonresponse and various imputation methods have been devised . However, most imputation methods concern with continuous traits while many interesting features are measured by binary or ordered categorical scales in sample survey. In this note. an imputation method for ignorable nonresponse in binary or ordered categorical traits is considered.

Semiparametric and Nonparametric Modeling for Matched Studies

  • Kim, In-Young;Cohen, Noah
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.10a
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    • pp.179-182
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    • 2003
  • This study describes a new graphical method for assessing and characterizing effect modification by a matching covariate in matched case-control studies. This method to understand effect modification is based on a semiparametric model using a varying coefficient model. The method allows for nonparametric relationships between effect modification and other covariates, or can be useful in suggesting parametric models. This method can be applied to examining effect modification by any ordered categorical or continuous covariates for which cases have been matched with controls. The method applies to effect modification when causality might be reasonably assumed. An example from veterinary medicine is used to demonstrate our approach. The simulation results show that this method, when based on linear, quadratic and nonparametric effect modification, can be more powerful than both a parametric multiplicative model fit and a fully nonparametric generalized additive model fit.

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Comparison of Methodologies for Characterizing Pedestrian-Vehicle Collisions (보행자-차량 충돌사고 특성분석 방법론 비교 연구)

  • Choi, Saerona;Jeong, Eunbi;Oh, Cheol
    • Journal of Korean Society of Transportation
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    • v.31 no.6
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    • pp.53-66
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    • 2013
  • The major purpose of this study is to evaluate methodologies to predict the injury severity of pedestrian-vehicle collisions. Methodologies to be evaluated and compared in this study include Binary Logistic Regression(BLR), Ordered Probit Model(OPM), Support Vector Machine(SVM) and Decision Tree(DT) method. Valuable insights into applying methodologies to analyze the characteristics of pedestrian injury severity are derived. For the purpose of identifying causal factors affecting the injury severity, statistical approaches such as BLR and OPM are recommended. On the other hand, to achieve better prediction performance, heuristic approaches such as SVM and DT are recommended. It is expected that the outcome of this study would be useful in developing various countermeasures for enhancing pedestrian safety.

Analysis on the Satisfaction Factors of Housing Performance and Residential Environment of Public Housing in Seoul (서울시 공공임대주택 주택성능과 주거환경 만족도에 미치는 영향요인)

  • Sung, Jin-Uk;Nam, Jin
    • Journal of Korea Planning Association
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    • v.54 no.3
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    • pp.49-62
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    • 2019
  • In order to balance with supply policy, public housing management and operation policies have been implemented in terms of housing welfare, but citizens have not yet achieved the results that the citizens are experiencing. The purpose of this study is to analysis the residential satisfaction of the including the housing performance through the characteristics of the public housing residents in Seoul. The data used in this study is based on the survey data of public housing panel survey in Seoul (2016). The study method used ordered logistic regression analysis based on the fact that dependent variables appeared as ordered responses. Major research results are as follows. Firstly, housing performance and residential satisfaction may not match. Even though the satisfaction of housing area, type, and management fee is high, satisfaction with residential environment is low if commuting distance, the number of small libraries, and hospitals are small. Secondly, it showed different characteristics of residential environment factors among types of public housing. Rather than focusing on supply, customized supply is needed considering characteristics of public housing types. Thirdly, the policy for public housing needs to be realized by a fair policy on the residential environment. It is necessary to contribute to better housing stability as a customized policy considering the local residential environment.

Case Studies on the Optimal Parameter Design with Respect to Categorial Characteristics (범주형 품질특성의 최적설계 사례연구)

  • Park, Jong-In;Bae, Suk-Joo;Kim, Man-Soo
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.32 no.3
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    • pp.135-141
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    • 2009
  • A variety of statistical methods are applied to model and optimize responses, related to product or system's quality, in terms of control and noise factors at design and manufacturing stages. Most of them assume continuous response variables but, assessing the performance of a product or system often involves categorical observations, such as ratings and scores. Although most previous works to deal with the categorical data provide sorhisticated response models and ensure unbiased outcomes, they require heavy computation to estimate the model parameters, as well as enough replications. In this study, we present some practical approaches for optimal parameter design with ordered categorical response when only a few or no replication is available. Two real-life examples are given to illustrate the presented methods.

The Effects of COVID-19 on Public Transportation Demand: The Case of Busan Metropolitan City (코로나19의 확산이 대중교통 수요변화에 미치는 영향요인 분석 - 부산광역시를 중심으로 -)

  • Minjeong KIM;Hoe Kyoung KIM
    • Journal of the Korean Association of Geographic Information Studies
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    • v.26 no.3
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    • pp.1-11
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    • 2023
  • COVID-19 has caused the dramatic reduction of public transportation demand in Busan Metropolitan City, that is, daily public transportation trips in 2020 dropped by approximately 920,000 trips from 2019 based on the public transportation card data. This study investigated the underlying factors affecting the public transportation demand discrepancy between before and after COVID-19 at the primary administration unit(i.e., Eup, Myeon, Dong) level with Ordered Logistic Regression model. Finding of this study is as follows. The primary administration units characterized with high ratio of welfare recipients, industrial area, and day boarders were heavily dependent on public transit, indicating little change in public transportation demand. On the other hands, the primary administration units which have high ratio of urban rail transit uses experienced significant reduction of public transportation demand. In conclusion, transportation policies taken under emergent situation such as COVID-19 need to take into account the region-based characteristics rather than unilateral ones.