• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.957-981
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• 2017

A generalized linear mixed model is an extension of a generalized linear model that allows random effect as well as provides flexibility in developing a suitable model when observations are correlated or when there are other underlying phenomena that contribute to resulting variability. We describe maximum likelihood estimation methods for logistic regression models that include random effects - the Laplace approximation, Gauss-Hermite quadrature, adaptive Gauss-Hermite quadrature, and pseudo-likelihood. Applications are provided with social science problems by analyzing the effect of mental health and life satisfaction on volunteer activities from Korean welfare panel data; in addition, we observe that the inclusion of random effects in the model leads to improved analyses with more reasonable inferences.

• International Commerce and Information Review
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• v.14 no.3
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• pp.31-47
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• 2012

The Eurozone was launched to set a goal on using the single currency perfectly in 1999. Using the Euro could get rid of exchange cost and cost of Foreign exchange risk management which was approximately 1% of each member's GDP. It was possible that members has maintained a stable level of inflation and stimulate investment and employment with low interest rate. In addition, they could lead to economic growth and investment as well as increase the Euro demand in financial market. Especially, members has used the Euro as the method of payment on trade each other so that the volume of trade among the Eurozone members has increased continuously which was called "the effect of single market." This paper analyzes the correlation between using the Euro and members' export/import by using random effect estimation and fixed effect estimation. As a result, Eurozone members can get export decreasing effect of 4.68% and import increasing effect of 10.5% respectively on average by using the Euro.

• Journal of Korea Port Economic Association
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• v.27 no.3
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• pp.161-177
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• 2011

Nowadays, a capital flow and intimacy of financial system among countries have been increasing in global financial environment. So it is easily possible that the risk of some countries which are in financial crisis infects other countries in the world. A recent global financial crisis reminds countries in East Asia of advancing the financial cooperation as well as financial integration. Countries in East Asia agreed with the Chiang Mai Initiative to prevent a recurrence of financial crisis in East Asia. A bilateral swap arrangement of the CMI has several purposes in order to offer foreign currency liquidity against economic crisis, remove the opportunity cost of foreign exchange reserve, push ahead the financial integration, increase the export-import logistics and so on. This paper analyzes the effect of financial cooperation in East Asia on the export-import logistics with random effect estimation and fixed effect estimation. As a result, each of country in East Asia is able to increase almost 10.3% of the export-import logistics on average.

• International Area Studies Review
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• v.14 no.1
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• pp.203-219
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• 2010

Nowadays an instability of the exchange rate on accounts of global finance crisis brings on a lot of an economic damage such as recession, decreasing of total trade and so on. However some countries which belong to be membership of the eurozone could escape economic slump shortly and easier than others. The reason for this is that they share with the Euro as a their own currency which is the second vehicle currency all of the world. This paper analyzes the correlation of joining the Euro zone and trade with pooled OLS, random effect estimation, and fixed effect estimation. A membership of the Euro zone are able to increase trade 11.3% ~ 25.3% one another on average since some country belongs to the Euro zone. It is very important for some countries which have a plan to affiliate the Euro zone sooner or later to realize economic effect because of a protection of the Euro zone as well as political power.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.33-40
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• 1994

The conditional expectation of a random variable in a multivariate normal random vector is a multiple linear regression on its predecessors. Using this fact, the least median of squares estimation method developed in a multiple linear regression is adapted to a multivariate data to identify influential observations. The resulting method clearly detect outliers and it avoids the masking effect.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.281-285
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• 2002

The observation fuzzy data in random effect and balanced designs for one way classification by using a the matrix formulation, we can estimate the fuzzy variance components for the ozone depletion example and test by the agreement index.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.523-542
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• 2007

In this paper we consider semiparametric random effect panel models that contain AR(p) disturbances. We derive the efficient score function and the information bound for estimating the slope parameters. We make minimal assumptions on the distribution of the random errors, effects, and the regressors, and provide semiparametric efficient estimates of the slope parameters. The present paper extends the previous work of Park et al.(2003) where AR(1) errors were considered.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.755-762
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• 2015

One of the main objectives of the U.S. Census Bureau is the proper estimation of median household income for small areas. These estimates have an important role in the formulation of various governmental decisions and policies. Since direct survey estimates are available annually for each state or county, it is desirable to exploit the longitudinal trend in income observations in the estimation procedure. In this study, we consider Fay-Herriot type small area models which include time-specific random effect to accommodate any unspecified time varying income pattern. Analysis is carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. We have evaluated our estimates by comparing those with the corresponding census estimates of 1999 using some commonly used comparison measures. It turns out that among three types of time-specific random effects the small area model with a time series random walk component provides estimates which are superior to both direct estimates and the Census Bureau estimates.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.3
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• pp.302-308
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• 2014

In this paper, a detailed investigation of the random vibration test is presented for strapdown inertial navigation systems (INS). The effect of the random vibration test has been studied from the point of view of navigation performance. The role of distribution functions and RMS value is represented to determine a feasible method to reject or reduce vibration related error in position and velocity estimation in inertial navigation. According to a survey conducted by the authors, this is the first time that the effect of the distribution function in vibration related error has been investigated in random vibration testing of INS. Recorded data of navigation grade INS is used in offline static navigation to examine the effect of different characteristics of random vibration tests on navigation error.