• 생물정신의학
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• 제3권1호
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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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• 제22권2호
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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.

• 응용통계연구
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• 제25권6호
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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.

• 대한수학회보
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• 제56권6호
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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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• 제29권6호
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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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• 제14권4호
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• pp.27-49
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• 1992

본호(本鎬)는 지난 몇년간 우리나라 주가(株價) 및 지가(地價)의 급변을 거품현상(現象)으로 해석하는 견해(見解)를 재검토(再檢討)하고 시장기본요인(市場基本要因)의 중요성을 강조하고자 하였다. 자산가격변화(資産價格變化)에 대한 자기실현적(自己實現的) 예측(豫測)에 바탕을 둔 거품이론(理論)으로 최근 우리나라의 주가(株價) 및 지가변동(地價變動)을 설명하기에는 여러가지 제약점이 있다. 지난 몇년간 우리나라 주가(株價) 및 지가(地價)의 급변을 거품현상이라고 주장하는 사람들은 거품의 존재유무(存在有無)에 대한 검증 없이 막연하게 자산가격의 급변 내지는 불규칙한 변동을 거품현상으로 해석하는 경향이 있으며 주가(株價) 및 지관(地慣)의 거품검증을 시행한 경우에도 검증과정에서 여러가지 계량경제학적(計量經濟學的) 문제점을 보이고 있고, 근본적으로는 검증모형(檢證模型)이 잘못 구체화되었다는 오특정(誤特定)(misspecification)의 문제를 안고 있다. 본고(本稿)에서는 지난 몇 년간 우리나라 지가(地價) 및 주가변화(株價變化)를 3저여건(低與件)의 도래와 퇴조에 따른 경제여건의 변화와 시장기본요인의 변화로 설명할 수 있음을 보였다. 우선 실질금리(實質金利), 성장율(成長率) 등 시장기본요인(市場基本要因)의 변화로 우리나라 주가 및 지가변화를 상당부분 설명할 수 있었으며, 다음으로는 이부문(二部門) 일반균형(一般均衡) 자산구성모형(資産構成模型)을 구성하여 유동성(游動性) 및 환율(換率)이 변화할 때 지가(地價)의 과잉변동현상(過剩變動現像)이 발생함을 보였다. 1986~89년간 지가(地價)의 급등(急騰)은 동(同) 기간중(期間中) 엔화절상(貨切上), 원화절상(貨切上) 및 유동성증가(流動性增加)(실질금리(實質金利)의 하락(下落))에 따른 것이며, 90년 이후 지가상승세(地價上昇勢)의 둔화(鈍化)는 동(同) 기간중(期間中) 엔화절하(貨切下), 원화절하(貨切下)로의 반전 및 유동성압박(流動性壓迫)에 따른 실질금리(實質金利)의 상승(上昇)과 밀접히 관련되어 있다.

• 응용통계연구
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• 제17권2호
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• pp.253-267
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• 2004

임상시험중에는 소아암연구에서와 같이 환자 중의 상당수에서 사망 또는 재발이 오랜 기간 일어나지 않고 완치된 것으로 보이는 경우가 있다. 이 경우 연구자는 생존함수의 전반적인 비교보다는 치료율의 비교에 더 관심이 있을 것이다. 본고에서는 치료율의 비교를 위한 여러 모수적, 비모수적 방법들을 소개하고, 생존분포, 치료율, 중도절단을 등을 다양하게 설정한 모의실험을 통하여 각 방법들의 검정력과 유의수준을 비교하였다.

• 한국항만경제학회지
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• 제34권3호
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• pp.183-200
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• 2018

본 연구는 1996년 10월부터 2017년 4월까지의 건화물선, 컨테이너선, 유조선 가격과 운임 자료를 사용하여 선가의 합리적 거품 유무를 검정하였다. 기존의 연구와 달리, 컨테이너선, 유조선 가격으로 실증분석 범위를 확장하여 모형설정 오류에서 자유로운 안정성에 기초한 안정성 검정과 공적분 검정을 활용하였다. 안정성 검정 결과, 유조선 가격에 거품이 존재하였으며, 공적분 검정은 건화물선과 컨테이너선의 가격에 거품이 포함되었다는 결과를 나타내었다. 이러한 실증분석 결과는 우리나라 해운기업이 저선가 시기에 선박을 확보하는 선박투자 전략을 채택해야하며, 이를 촉진하기 위한 정부의 금융 지원과 안정적인 선복량 확보 정책 수립의 필요성을 시사한다.

• The Journal of Asian Finance, Economics and Business
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• 제8권5호
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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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• 제30권4호
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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.