• The Korean Journal of Applied Statistics
• /
• v.23 no.6
• /
• pp.1057-1065
• /
• 2010

In this paper we discuss some cautionary notes in using the Pearson chi-squared test statistic for the goodness-of-fit of the ordinal response model. If a model includes continuous type explanatory variables, the resulting table from the t of a model is not a regular one in the sense that the cell boundaries are not fixed but randomly determined by some other criteria. The chi-squared statistic from this kind of table does not have a limiting chi-square distribution in general and we need to be very cautious of the use of a chi-squared type goodness-of-t test. We also study the limiting distribution of the chi-squared type statistic for testing the goodness-of-t of cumulative logit models with ordinal responses. The regularity conditions necessary to the limiting distribution will be reformulated in the framework of the cumulative logit model by modifying those of Moore and Spruill (1975). Due to the complex limiting distribution, a parametric bootstrap testing procedure is a good alternative and we explained the suggested method through a practical example of an ordinal response dataset.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.2
• /
• pp.471-479
• /
• 2007

This paper discusses about how to build up mixed-effects model for analysing ordinal response data by using cumulative logits. Random factors are assumed to be coming from the designed sampling scheme for choosing observational units. Since the observed responses of individuals are ordinal, a proportional odds model with two random effects is suggested. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

• Communications for Statistical Applications and Methods
• /
• v.16 no.4
• /
• pp.697-705
• /
• 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.

• Communications for Statistical Applications and Methods
• /
• v.24 no.1
• /
• pp.81-96
• /
• 2017

Cumulative logit random effects models are typically used to analyze longitudinal ordinal data. The random effects covariance matrix is used in the models to demonstrate both subject-specific and time variations. The covariance matrix may also be homogeneous; however, the structure of the covariance matrix is assumed to be homoscedastic and restricted because the matrix is high-dimensional and should be positive definite. To satisfy these restrictions two Cholesky decomposition methods were proposed in linear (mixed) models for the random effects precision matrix and the random effects covariance matrix, respectively: modified Cholesky and moving average Cholesky decompositions. In this paper, we use these two methods to model the random effects precision matrix and the random effects covariance matrix in cumulative logit random effects models for longitudinal ordinal data. The methods are illustrated by a lung cancer data set.

• Journal of Fisheries and Marine Sciences Education
• /
• v.27 no.6
• /
• pp.1693-1703
• /
• 2015

This study is aimed to retain objectivity and generality against consumer perception about seafood of Hebei Spirit oil spill incident area, and analyze gap of consumer perception in according to confidence level of seafood using ordinal logit model. As results, This article have three implications. First, Consumer don't have high confidence level in comparison with previous research. Second, It is important to retain confidence of quality for purchasing seafood of oil spill incident area. Third, Consumer perception is improved positively against seafood of oil spill area. But This study has limits that don't take regional opinion and situation into account.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.6
• /
• pp.69-79
• /
• 2022

The purpose of the study is to look into the likelihood of private sector enterprises going bankrupt due to COVID-19 pandemic-related issues. The data for this study was taken from the World Bank's Enterprise Survey, which was intended to assess the impact of the COVID-19 pandemic on the business sector. This study uses the Ordinal Logit Method to analyze the model with dependent variables having ordinal values. The determinants reflect business performance, innovation, business relationships, and government support. According to the estimation results, a lower probability of business closures, illiquidity, and payment delays are found in businesses that maintain sales growth, operating hours, temporary workers, product portfolio, consumer demand, and input supply. Meanwhile, the increase in online business activities and receiving support from financial institutions and the government do not help businesses reduce the risk. Moreover, higher survival is found in manufacturing and developing countries. This implies the fragility of businesses in the retail and service sectors, especially for mega-enterprises in developed countries. In addition, the negative impact of the COVID-19 pandemic on businesses in Europe and West Asia is less severe than in other regions. The results imply policies to support the private sector during the pandemic, such as increasing labor market flexibility or rapidly implementing supportive policies.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.3D
• /
• pp.275-281
• /
• 2012

The aim of this study is to suggest the development direction of future space management. It is not a simple question that why people visit specific place. People visit a place for escape from routine, to learn from other people, spend a time with their friends or lover, to take a rest and get some fresh idea, to relieve stress, and for shopping. It is depend on a various visit motivation with complicated a psychological phenomenon. Visitor participate in choice a specific place by various motivation and purpose. Generally, visitor motivation accepted understanding visitor behavior and process of selected a place. Understanding that why people visit specific place can use a marketing and policy making of visit place so we need to study about visitor motivation. Data were collected through offline surveys from 501 people who have visited four survey place. Empirically analyzed the determinations of visitor motivation and estimate the place by using Ordinal Logit Model.

• The Korean Journal of Applied Statistics
• /
• v.10 no.1
• /
• pp.151-161
• /
• 1997

An easy Monte Carlo Gibbs sampling approach is suggested for Bayesian analysis of cumulative logit models for ordinal polytomous data. Because in the cumulative logit model the posterior conditional distributions of parameters are not given in convenient forms for random sample generation, appropriate latent variables are introduced into the model so that in the new model all the conditional distributions are given in very convenient forms for implementation of the Gibbs sampler.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.2D
• /
• pp.287-293
• /
• 2009

The aim of this paper is to analyze resettlement cost and decision factors of resettlement since Redevelopment Promotion Projects. Range of resettlement cost was averagely increased 204% by using actual data. Consequently, the research is operated for aboriginal people in these areas by a questionnaire. The questionnaire ask a payment range of the resettlement cost with 4 stages; 150% and less, 180% and less, 200% and less, excess of 200%. Research scope is consist of Seo-kumsa, Civil Park, Chung-mu and Young-do. These areas are redevelopment of Busan metropolitan city. Resettlement is come under the influence of the resettlement cost and many factors by each specific character. In many alternatives for resettlement, understanding the reason why aboriginal peoples select a certain alternative and if we actualize the proper alternative, aboriginal peoples' resettlement ratio will be increased. Moreover it ask housing characteristic, housing life pattern for understanding aboriginal peoples' characteristic. Also data analysis model is ordinal logistic model'. In analysis result, resettlement cost is 150% of aboriginal assets. and significance parameter is sex, job, income, region, affection, attachment, housing possession type, size and others have influence on aboriginal peoples' resettlement.

• IE interfaces
• /
• v.15 no.2
• /
• pp.209-217
• /
• 2002

Quality of Service(QoS) in the telecommunications can be evaluated by analyzing the opinion data which result from the surveyed opinions of respondents and quantify subjective satisfaction on the QoS from the customers' viewpoints. For analyzing the opinion data, MOS(mean opinion score) method and Cumulative Probability Curve method are often used. The methods are based on the scoring method, and therefore, have the intrinsic deficiency due to the assignment of arbitrary scores. In this paper, we propose an analysis method of the opinion data using logit models which can be used to analyze the ordinal categorical data without assigning arbitrary scores to customers' opinion, and develop an analysis procedure considering the usage of procedures provided by SAS(Statistical Analysis System) statistical package. By the proposed method, we can estimate the relationship between customer satisfaction and network performance parameters, and provide guidelines for network planning. In addition, the proposed method is compared with Cumulative Probability Curve method with respect to prediction errors.