• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.413-420
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• 2008

This paper suggests a marginal logit mixed-effects for analyzing repeated binary response data. Since binary repeated measures are obtained over time from each subject, observations will have a certain covariance structure among them. As a plausible covariance structure, 1st order auto-regressive correlation structure is assumed for analyzing data. Generalized estimating equations(GEE) method is used for estimating fixed effects in the model.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.123-130
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• 2006

This paper discusses about how to build up a mixed-effects model using cumulative logits when some factors are fixed and others are random. Location effects are considered as random effects by choosing them randomly from a population of locations. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

• Environmental and Resource Economics Review
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• v.14 no.4
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• pp.867-891
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• 2005

The purpose of this study is estimating the recreation benefits of the largest 10 dam reservoirs in Korea. A mixed logit or random parameters log it model is constructed and estimated. Not only the recreation value of each dam lake but also the values of the main characteristics of the lakes such as the amount of water reserved, and the availability of boating and fishing are estimated. It is shown that recreation value is not less than other benefit such as irrigation, industrial, municipal use, hydro power, or even flood control benefit.

• Journal of Korean Society of Forest Science
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• v.95 no.6
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• pp.768-777
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• 2006

The purpose of this study lies in formulating the landscape preference model using a conditional logit that involves the effect of visual elements as well as landscape itself on landscape preferences. To measure landscape preferences, a photo-questionnaire composed of paired photographs of the Cairngorms National Park of Scotland and the Jirisan National Park of Korea was distributed to visitors to the Jirisan National Park of Korea. Visual elements of landscape quantitatively measured by photogrammetry were reduced to orthogonal principal components that were subsequently used as explanatory variables in a conditional logit. As a result, the mixed conditional logit including the effect of landscape itself satisfied the Independence of Irrelevant Alternatives (IIA) property and showed reliable goodness of fit (${\rho}^2=0.25$). It was concluded that the mixed conditional logit including the effect of landscape itself was appropriate for landscape preference model rather than usual conditional logit excluding the effect.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.379-386
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• 2004

This paper deals with a mixed logit model for vaccination data. The effect of a newly developed vaccine for a certain chicken disease can be evaluated by a noninfection rate after injecting chicken with the disease vaccine. But there are a lot of factors that might affect the noninfecton rate. Some of these are fixed and others are random. Random factors are sometimes coming from the sampling scheme for choosing experimental units. This paper suggests a mixed model when some fixed factors need to have different experimental sizes by an experimental design and illustrates how to estimate parameters in a suggested model.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.121-126
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• 2004

This paper discusses about how to build up a mixed-effects model using cumulative logits when there are some factors are fixed and others are random. Random factors are assumed to be coming from a two-way nested design for choosing individuals or experimental units to apply treatments. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

• Environmental and Resource Economics Review
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• v.19 no.3
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• pp.547-569
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• 2010

The purpose of this study is to estimate the benefits associated with the opportunities of multiple destination trips using a mixed logit model. The opportunities that affect the destination choice can be one of the external environments of recreational sites. The data for this study were taken from a survey conducted to the multiple destination trip participants at Seorak National Park in Korea. The results of the research show the specific values of complementary opportunities in the study area.

• Asia pacific journal of information systems
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• v.15 no.4
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• pp.165-192
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• 2005

This study introduces how to estimate the monetary value of intellectual capital of a public research institute by incorporating a non-market valuation technique, the choice experiments(CE). CE is a survey-based environmental valuation technique that has increasingly been popular over the last decade. The members of institute E, a typical type of public research institutes in Korea, were surveyed, before the data were fit to the conditional logit and mixed logit models. The total value of the institute's intellectual capital was estimated at approximately W3,377 billion for the year 2003. The institute's human, structural and relational capitals that comprise the intellectual capital were estimated at W18.7 billion, W10.7 billion and W4.4 billion respectively, for each of the components' index values improving by 1%. The human capital was placed a higher value than the other two. The study also shows that CE is a flexible technique that enables the researcher to estimate the monetary value of the intellectual capital whatever the index values of the component capitals and to interpret model estimation results more in depth by incorporating the mixed logit, a state-of-the-art discrete choice model, than the conventional conditional logic.

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

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.129-137
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• 2002

This paper suggests a generalized logit model with mixed effects for analysing frequency data in multi-contingency table. In this model nominal response variable is assumed to be polychotomous. When some factors are fixed but considered as ordinal and others are random, this paper shows how to use baseline-category logits to incoporate the mixed-effects of those factors into the model. A numerical algorithm was used to estimate model parameters by using marginal log-likelihood.