• Korean Journal of Biological Psychiatry
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• v.3 no.1
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• pp.14-21
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• 1996

Molecular genetic approaches contribute to the understanding of the underlying genetic mechanism for schizophrenia. Currently genetic evidence rests on molecular genetic methods. However, the result are contradictory and somewhat confusing due to genetic heterogeneity, incomplete penetrance, misspecification of genetic model. It is expected that molecular genetics could provide key answers to the genetic cause of schizophrenia. The purpose of this article is to call attention of the readers to heterogeneity, linkage, association, basic molecular genetic methods and genetic markers and to the need far further research. It is the author's hope thai continuous research on the molecular genetics con provide clinicians with better understanding of the schizophrenia.

• East Asian Economic Review
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• v.22 no.2
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• pp.141-176
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• 2018

The paper analyzes and compares the effects of domestic monetary policy using DSGE, DSGE-VAR, and VAR based on a two-country open economy model of Korea and the U.S. According to impulse response analysis, a domestic interest rate hike raises won value in the case of DSGE and DSGE-VAR models, while in the case of the unrestricted VAR model, it lowers won value. In the marginal data density standard, DSGE-VAR (${\mu}=1$) is superior to DSGE or Bayesian VAR over the sample period. Conversely, in the in-sample RMSE criterion, especially for the won/dollar exchange rate, VARs are superior to DSGE or DSGE-VAR. It is necessary to study further if these differences are caused by model misspecification or omitted variable bias.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1037-1047
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• 2012

The generalized linear mixed model(GLMM) is widely used in fitting categorical responses of clustered data. In the numerical approximation of likelihood function the normality is assumed for the random effects distribution; subsequently, the commercial statistical packages also routinely fit GLMM under this normality assumption. We may also encounter departures from the distributional assumption on the response variable. It would be interesting to investigate the impact on the estimates of parameters under misspecification of distributions; however, there has been limited researche on these topics. We study the sensitivity or robustness of the maximum likelihood estimators(MLEs) of GLMM for counts data when the true underlying distribution is normal, gamma, exponential, and a mixture of two normal distributions. We also consider the effects on the MLEs when we fit Poisson-normal GLMM whereas the outcomes are generated from the negative binomial distribution with overdispersion. Through a small scale Monte Carlo study we check the empirical coverage probabilities of parameters and biases of MLEs of GLMM.

• Bulletin of the Korean Mathematical Society
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• v.56 no.6
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• pp.1467-1483
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• 2019

This paper analyzes a robust optimal reinsurance and investment strategy for an Ambiguity-Averse Insurer (AAI), who worries about model misspecification and insists on seeking robust optimal strategies. The AAI's surplus process is assumed to follow a jump-diffusion model, and he is allowed to purchase proportional reinsurance or acquire new business, meanwhile invest his surplus in a risk-free asset and a risky-asset, whose price is described by an Ornstein-Uhlenbeck process. Under the criterion for maximizing the expected exponential utility of terminal wealth, robust optimal strategy and value function are derived by applying the stochastic dynamic programming approach. Serval numerical examples are given to illustrate the impact of model parameters on the robust optimal strategies and the loss utility function from ignoring the model uncertainty.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.641-653
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• 2022

Penalized least squares methods are important tools to simultaneously select variables and estimate parameters in linear regression. The penalized maximum likelihood can also be used for the same purpose assuming that the error distribution falls in a certain parametric family of distributions. However, the use of a certain parametric family can suffer a misspecification problem which undermines the estimation accuracy. To give sufficient flexibility to the error distribution, we propose to use the symmetric log-concave error distribution with LASSO penalty. A feasible algorithm to estimate both nonparametric and parametric components in the proposed model is provided. Some numerical studies are also presented showing that the proposed method produces more efficient estimators than some existing methods with similar variable selection performance.

• KDI Journal of Economic Policy
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• v.14 no.4
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• pp.27-49
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• 1992

This paper stresses the role of market fundamentals rather than bubbles in explaining Korea's recent experience of large fluctuations of stock and real estate prices. The bubble story that emphasizes the self-fulfilling prophecies of investors seems to be inappropriate to explain the recent changes of assets prices in Korea. Those who argue for bubble phenomenon in Korea tend to interpret the volatile movements of assets prices as some form of bubbles, but without implementing a rigorous test on the presence of bubbles. Even when some bubble tests are carried out, such studies exhibit various econometric problems in testing. More seriously, they suffer from the misspecification problems in setting up a market model. This paper has shown that Korea's recent changes in assets prices could be explained by changes in market fundamentals according to the emergence and the subsequent fading of 'three lows'. First, it tried to explain changes in assets prices by changes in such market fundamentals as real interest rates and economic growth. Second, it showed that the real estate prices overshoot when the liquidity and exchange rates change, using the two-sector general equilibrium portfolio balance model. It is argued that the rapid rise in real estate prices during 1986-89 stems from Yen's and Won's appreciation $vis-{\grave{a}}-vis$ the U.S. dollar and liquidity expansion (or decreases in real interest rates), while the downturn in real estate prices since 1990 is associated with Yen's and Won's depreciation $vis-{\grave{a}}-vis$ the U.S. dollar and rises in real interest rates in reflection of the excess demand for liquidity.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.253-267
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• 2004

In some clinical trials, one may see that a significant fraction of patients are cured and their original disease does not recur even after termination of treatment and pro-longed follow-up. This situation occurs frequently in pediatric cancer trials where there are excellent therapeutic results. In such cases, interest concentrated on the difference of cure rates rather than other types of differences in failure distributions. Various authors have investigated the parametric and nonparametric methods for testing the difference of cure rates. In this study, we compare by simulation the power and size of a parametric test and five nonparametric tests in a various range of the alternatives, censoring rates and cure rates. Our objectives are to determine if any test was preferable on the basis of size and power in various situation, and to investigate the effect of the model misspecification.

• Journal of Korea Port Economic Association
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• v.34 no.3
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• pp.183-200
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• 2018

This study empirically tests the presence of rational bubbles in the ship prices using time series data from October 1996 to April 2017. To detect the existence of ship prices' rational bubbles, we use integration and cointegration tests, which were proposed by Campbell and Shiller(1987) and Diba and Grossman(1988), for circumventing misspecification of ship price model and applying the bubble test to nonstationary time series. The result of integration test supports existence of tanker price's rational bubble. The co-integration test also shows that drybulk ship and containership prices have been overvalued relative to the market fundamental, drybulk and container freight rates, due to non-stationary rational bubbles. These results provide Korean shipping industry and authorities implications that anticyclical ship investment and long-term and steady fleet capacity expansion policy are needed.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.573-581
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• 2021

The current study analyzed the impacts of fiscal decentralization (FD) on the economic stability of Pakistan. This study used time series data from 1981 to 2017. The collected data was first passed through the unit root analysis. ARDL estimation techniques were employed to scrutinize the data where long-run associations were tested through Wald F-statistics. The long-run estimates were extracted by applying Ordinary Least Square, and error correction mechanisms were employed to find the speed of adjustment for disequilibria between the long and the short run. Wald F-statistics confirmed the existence of long-run cointegration. Long-run elasticities suggested that fiscal decentralization because of limited institutional capabilities of provincial governments failed in bringing stability in the economy of Pakistan. Similarly, transparency issues and misspecification of projects hinder the outcome of investment to stabilize the economy. High service payments on debt cut the amount that can be used for skills improvements and destabilize the economy. High Population growth puts pressure on infrastructure and reduces production capacity, ultimately destabilizing the economy by increasing unemployment and inflation. Based on these findings, the government is suggested to improve the institutional capacity of lower governments for the desired outcome of power devolution.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.389-402
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• 2023

Multivariate or clustered failure time data often occur in many medical, epidemiological, and socio-economic studies when survival data are collected from several research centers. If the data are periodically observed as in a longitudinal study, survival times are often subject to various types of interval-censoring, creating multivariate interval-censored data. Then, the event times of interest may be correlated among individuals who come from the same cluster. In this article, we propose a unified linear regression method for analyzing multivariate interval-censored data. We consider a semiparametric multivariate accelerated failure time model as a statistical analysis tool and develop a generalized Buckley-James method to make inferences by imputing interval-censored observations with their conditional mean values. Since the study population consists of several heterogeneous clusters, where the subjects in the same cluster may be related, we propose a generalized estimating equations approach to accommodate potential dependence in clusters. Our simulation results confirm that the proposed estimator is robust to misspecification of working covariance matrix and statistical efficiency can increase when the working covariance structure is close to the truth. The proposed method is applied to the dataset from a diabetic retinopathy study.