• Journal of applied mathematics & informatics
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• 제7권2호
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• pp.653-664
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• 2000

The problem of inference about the unknown change-point with a change in mean is considered. We suggest a nonparametric change-point estimator using window and prove its consistency when the errors are from the distribution with the mean zero and the common variance. a comparison study is done by simulation on the mean, the variance, and the proportion of matching the true change-points.

• Communications for Statistical Applications and Methods
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• 제23권5호
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• pp.411-421
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• 2016

Large frequency limiting distributions of two errors in realized covariance are investigated under noisy and non-synchronous high frequency sampling situations. The first distribution characterizes increased variance of the realized covariance due to noise for large frequency and the second distribution characterizes decreased variance of the realized covariance due to discretization for large frequency. The distribution of the combined error enables us to determine the sampling frequency which depends on a nuisance parameter. A consistent estimator of the nuisance parameter is proposed.

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.467-478
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• 1999

This paper deals with the problem of change-point estimation where there is one level change in location with iid errors. A change-point estimator using rank average is proposed with the proof of its consistency. A comparison study of various change-point estimators is done by simulation on the mean the proportion and the variance when the errors are from the normal and the double exponential distributions.

• Journal of the Korean Statistical Society
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• 제23권1호
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• pp.115-134
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• 1994

Polynomial errors-in-variables model with one predictor variable and one response variable is defined and an estimator of model is derived following the Booth's linear model estimation procedure. Since polynomial model is nonlinear function of the unknown regression coefficients and error-free predictors, it is nonlinear model in errors-in-variables model. As a result of applying linear model estimation method to nonlinear model, some additional assumptions are necessary. Hence, an estimator is derived under the assumption that the error variances are decrasing as sample size increases. Asymptotic propoerties of the derived estimator are provided. A simulation study is presented to compare the small sample properties of the derived estimator with those of OLS estimator.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제34권4호
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• pp.236-241
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• 2023

Objectives: According to the 10th revision of the International Classification of Diseases, the main categories of tic disorders (F95.0, F95.1, and F95.2) follow a diagnostic hierarchy based on the duration and diversity of tic symptoms. The present study investigated the use of this diagnostic hierarchy in real-world clinical practice. Methods: Based on the National Health Insurance Service-National Health Information Database, the diagnosis of transient tic disorder (F95.0) made after a diagnosis of chronic motor or vocal tic disorder (F95.1) or Tourette's syndrome (F95.2) and diagnosis of chronic motor or vocal tic disorder (F95.1) made after a diagnosis of Tourette's syndrome (F95.2) were referred to as type A errors. The diagnosis of transient tic disorder (F95.0) repeated after a period of >12 months was referred to as type B error. Demographic and clinical differences according to the diagnostic error types were analyzed using analysis of variance, Student's t-tests, and chi-squared tests. Results: Most participants (96.5%) were without errors in the diagnosis of tic disorders. Higher proportions of males (p=0.005) and antipsychotic prescriptions (p<0.001) were observed in patients with type A or B diagnostic errors. A higher proportion of health insurance holders was observed among those with type A errors (p=0.027). Conclusion: Errors were absent in majority of the tic diagnoses in real-world clinical practice in terms of the diagnostic hierarchy.

• Spatial Information Research
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• 제21권2호
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• pp.45-54
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• 2013

해석적 또는 시뮬레이션 오차 모델은 공간 데이터가 가지는 위치오차의 분포를 설명 하는데 유용하다. 그러나 두 오차 모델은 위치오차를 모델링을 하기위하여 다른 접근 방법을 이용하므로 정의된 조건 내에서 올바른 위치오차를 예측 하는지 확인하는 내적 검증을 필요로 한다. 이에 본 논문은 오차타원과 에러밴드 모델을 이용하여 제시한 포인트와 라인 세그먼트 시뮬레이션 오차 모델을 내부적으로 검증하는 방법을 제안하였다. 시뮬레이션 오차 모델은 분산-공분산 행렬(variance-covariance matrix)의 변수에 의해 규정된 확률분포에 따라 몬테카를로 시뮬레이션을 이용하여 위치오차들을 생성한다. 검증절차에서는 시뮬레이션 모델에 의한 위치오차의 집합을 해석적 오차 모델에 의한 이론적 위치오차와 비교하였다. 결과적으로 제안된 시뮬레이션 오차 모델은 정의된 위치오차에 따라 동일한 공간 데이터의 위치적 불확실성을 실현함을 확인할 수 있었다.

• Genomics & Informatics
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• 제20권1호
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• pp.9.1-9.6
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• 2022

Mendelian randomization (MR) uses genetic variation as a natural experiment to investigate the causal effects of modifiable risk factors (exposures) on outcomes. Two-sample Mendelian randomization (2SMR) is widely used to measure causal effects between exposures and outcomes via genome-wide association studies. 2SMR can increase statistical power by utilizing summary statistics from large consortia such as the UK Biobank. However, the first-order term approximation of standard error is commonly used when applying 2SMR. This approximation can underestimate the variance of causal effects in MR, which can lead to an increased false-positive rate. An alternative is to use the second-order approximation of the standard error, which can considerably correct for the deviation of the first-order approximation. In this study, we simulated MR to show the degree to which the first-order approximation underestimates the variance. We show that depending on the specific situation, the first-order approximation can underestimate the variance almost by half when compared to the true variance, whereas the second-order approximation is robust and accurate.

• Communications for Statistical Applications and Methods
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• 제19권1호
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• pp.177-182
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• 2012

A structural break in the level as well as in the innovation variance has often been exhibited in economic time series. In this paper we propose robust unit root tests based on a sign-type test statistic when a time series has a shift in its level and the corresponding volatility. The proposed tests are robust to a wide class of partially stationary processes with heavy-tailed errors, and have an exact binomial null distribution. Our tests are not affected by the size or location of the break. We set the structural break under the null and the alternative hypotheses to relieve a possible vagueness in interpreting test results in empirical work. The null hypothesis implies a unit root process with level shifts and the alternative connotes a stationary process with level shifts. The Monte Carlo simulation shows that our tests have stable size than the OLSE based tests.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.118-128
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• 2017

Uncertain factors in finical markets make the prediction of future returns and risk of asset much difficult. In this paper, a model,assuming the admissible errors on expected returns and risks of assets, assisted in the multiperiod mean variance portfolio selection problem is built. The model considers transaction costs, upper bound on borrowing risk-free asset constraints, cardinality constraints and threshold constraints. Cardinality constraints limit the number of assets to be held in an efficient portfolio. At the same time, threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Because of these limitations, the proposed model is a mix integer dynamic optimization problem with path dependence. The forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, to evaluate the model, our result of a meaning example is compared to the terminal wealth under different constraints.

• 응용통계연구
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• 제25권4호
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• pp.681-695
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• 2012

In this paper, a family of estimators for the finite population variance investigated by Srivastava and Jhajj (1980) is studied under two different situations of random non-response considered by Tracy and Osahan (1994). Asymptotic expressions for the biases and mean squared errors of members of the proposed family are obtained; in addition, an asymptotic optimum estimator(AOE) is also identified. Estimators suggested by Singh and Joarder (1998) are shown to be members of the proposed family. A correction to the Singh and Joarder (1998) results is also presented.