• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.235-240
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• 2013

Generalized linear mixed models(GLMMs) are frequently used for the analysis of longitudinal categorical data when the subject-specific effects is of interest. In GLMMs, the structure of the random effects covariance matrix is important for the estimation of fixed effects and to explain subject and time variations. The estimation of the matrix is not simple because of the high dimension and the positive definiteness; subsequently, we practically use the simple structure of the covariance matrix such as AR(1). However, this strong assumption can result in biased estimates of the fixed effects. In this paper, we introduce Bayesian modeling approaches for the random effects covariance matrix using a modified Cholesky decomposition. The modified Cholesky decomposition approach has been used to explain a heterogenous random effects covariance matrix and the subsequent estimated covariance matrix will be positive definite. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using these methods.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.179-187
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• 2016

The fixed effects panel probit model faces "incidental parameters problem" because it has a property that the number of parameters to be estimated will increase with sample size. The maximum likelihood estimation fails to give a consistent estimator of slope parameter. Unlike the panel regression model, it is not feasible to find an orthogonal reparameterization of fixed effects to get a consistent estimator. In this note, a hierarchical Bayesian model is proposed. The model is essentially equivalent to the frequentist's random effects model, but the individual specific effects are estimable with the help of Gibbs sampling. The Bayesian estimator is shown to reduce reduced the small sample bias. The maximum likelihood estimator in the random effects model is also efficient, which contradicts Green (2004)'s conclusion.

• Journal of Korea Trade
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• v.23 no.2
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• pp.76-87
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• 2019

Purpose - This study empirically analyzes the effects of the European Union-South Korea Free Trade Agreement on Korean exports in major sectors. Design/Methodology - This study is based on the augmented gravity model with a panel data set covering 51 countries between the years 2000 and 2015. Findings - Main findings of the present study is that the agreement has affected the chemical sector the most. Fixed effects estimation predicted a positive trade effect of 38.3%, while Poisson maximum likelihood estimation predicted an impact of 4.75% in the chemical export sector. Regression results for the other sectors only show insignificant effects. Originality/value - The findings imply that the effects of the EU-South Korea free trade agreement on the Korean exports are quite specific compared to the European ones, meaning that the Korean government should focus on sector-specific programs to maximize the welfare benefits of the free trade agreement.

• Journal of Labour Economics
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• v.43 no.1
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• pp.35-57
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• 2020

Using the Korea Labor and Income Panel Study(KLIPS), this study analyzes the effects of profit sharing on employer-provided training. The estimation results of the fixed effect model that controls for endogeneity show that the workers of profit-sharing firms have a 6.7%-6.8%p higher probability of receiving employer-provided training than the workers of firms without profit sharing. They also show that the workers of profit-sharing firms have a 3.3%p higher likelihood of having employer-provided OJT than their counterparts. The impacts of profit-sharing on employer-provided training appear consistently regardless of the estimation models and in the subsamples. These findings support the hypothesis that profit-sharing promotes employer-provided training.

• 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.

• The Journal of Industrial Distribution & Business
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• v.8 no.2
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• pp.5-10
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• 2017

Purpose - The purpose of this paper was to analyze the Chinese government's announcement of the RMB's appreciation on July 1, 2010, and its aim was to ascertain whether the appreciation has affected Chinese export prices by empirically measuring the degree of the exchange rate pass-tough on those prices. Research design, data, and methodology - Using 73 HS trade categories with cross-industry and time-series data, the panel estimation of a fixed-effects model has been applied to measure the degree and stability of any exchange rate pass-through effects. The estimation results show that the export prices of most trade categories were affected by the exchange rate changes. The pass-through effect was generally small, at about -0.485, and statistically significant in most export prices. Results - The empirical results indicate that China would lose its advantage and competitiveness in export if the RMB were appreciated continuously and rapidly because its export goods would no longer operate under strong monopolistic competition. Conclusions - The implications for China's exchange rate policy suggest that it would be better for the RMB to appreciate slowly and gradually rather than radically. It is clear that it would be allow the capital free flow in Chinese overall economic interest to reduce the continuous appreciation pressure on the currency and pave the way for improvements in export distribution competitiveness.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.125-134
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• 2003

Standard estimation methods for linear mixed models are sensitive to influential observations. However, tools and concepts for linear mixed model diagnostics are rudimentary until now and research is heavily demanded in linear mixed models. In this paper, we consider two diagnostics to evaluate the effects of individual observations in the estimation of fixed effects for linear mixed models. Those are Cook's distance and COVRATIO. Results of our limited simulation study suggest that the Cook's distance is not good statistical quantity in linear mixed models. Also calibration point for COVRATIO seems to be quite conservative.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.381-384
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• 2009

In the Wyner-Ziv coding, compression performance highly depends on the quality of the side information since better quality of side information brings less channel noise and less parity bit. However, as decoder generates side information without any knowledge of the current Wyner-Ziv frame, it doesn't have optimal criterion to decide which block is more advantageous to generate better side information. Hence, in general, fixed block size motion estimation (ME) is performed in generating side information. By the fixed block size ME, the best coding performance cannot be attained since some blocks are better to be motion estimated in different block sizes. Therefore if there is a way to find appropriate ME block of each block, the quality of the side information might be improved. In this paper, we investigate the effects of variable block sizes of ME in generating side information.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1327-1334
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• 2008

Mixed effect binomial regression models are widely used for analysis of correlated count data in which the response is the result of a series of one of two possible disjoint outcomes. In this paper, we consider kernel extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.1
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• pp.85-93
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• 2015

Previous studies have estimated crash prediction models with the fixed effect model which assumes the fixed value of coefficients without considering characteristics of each intersections. However the fixed effect model would estimate under estimation of the standard error resulted in over estimation of t-value. In order to overcome these shortcomings, the random effect model can be used with considering heterogeneity of AADT, geometric information and unobserved factors. In this study, data collections from 89 intersections in Daejeon and estimates of crash prediction models were conducted using the random and fixed effect negative binomial regression model for comparison and analysis of two models. As a result of model estimates, AADT, speed limits, number of lanes, exclusive right turn pockets and front traffic signal were found to be significant. For comparing statistical significance of two models, the random effect model could be better statistical significance with -1537.802 of log-likelihood at convergence comparing with -1691.327 for the fixed effect model. Also likelihood ration value was computed as 0.279 for the random effect model and 0.207 for the fixed effect model. This mean that the random effect model can be improved for statistical significance of models comparing with the fixed effect model.