• The Korean Journal of Physiology and Pharmacology
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• 제16권2호
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• pp.97-106
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• 2012

The pharmacokinetics/pharmacodynamics analysis software NONMEM$^{(R)}$ output provides model parameter estimates and associated standard errors. However, the standard error of empirical Bayes estimates of inter-subject variability is not available. A simple and direct method for estimating standard error of the empirical Bayes estimates of inter-subject variability using the NONMEM$^{(R)}$ VI internal matrix POSTV is developed and applied to several pharmacokinetic models using intensively or sparsely sampled data for demonstration and to evaluate performance. The computed standard error is in general similar to the results from other post-processing methods and the degree of difference, if any, depends on the employed estimation options.

• Management Science and Financial Engineering
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• 제9권1호
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• pp.4-10
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• 2003

We introduce a new estimator of the uncertainty of a jackknife estimate of standard error: the jack-knife-after-jackknife (JAJ). Using Monte Carlo simulation, we assess the accuracy of the JAJ in a variety of settings defined by statistic of interest, data distribution, and sample size. For comparison, we also assess the accuracy of the jackknife-after-bootstrap (JAB) estimate of the uncertainty of a bootstrap standard error. We conclude that the JAJ provides a useful new supplement to Tukey's jackknife, and the combination of jackknife and JAJ provides a useful alternative to the combination of bootstrap and JAB.

• Management Science and Financial Engineering
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• 제9권1호
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• pp.1-10
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• 2003

We introduce a new estimator of the uncertainty of a jackknife estimate of standard error: the jack-knife-after-jackknife (JAJ). Using Monte Carlo simulation, we assess the accuracy of the JAJ in a variety of settings defined by statistic of interest, data distribution, and sample size. For comparison, we also assess the accuracy of the jackknife-after-bootstrap (JAB) estimate of the uncertainty of a bootstrap standard error. We conclude that the JAJ provides a useful new supplement to Tukey's jackknife, and the combination of jackknife and JAJ provides a useful alternative to the combination of bootstrap and JAB.

• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.895-903
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• 2009

The penalized partial likelihood based on restricted maximum likelihood method has been widely used for the inference of frailty models. However, the standard-error estimate for frailty parameter estimator can be downwardly biased. In this paper we show that such underestimation can be corrected by using hierarchical likelihood. In particular, the hierarchical likelihood gives a statistically efficient procedure for various random-effect models including frailty models. The proposed method is illustrated via a numerical example and simulation study. The simulation results demonstrate that the corrected standard-error estimate largely improves such bias.

• 응용통계연구
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• 제30권3호
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• pp.453-462
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• 2017

성향점수 매칭은 관찰연구에서 처리효과 추정 시 혼란변수에 의한 편의를 줄이기 위해 자주 사용되는 방법이다. 매칭을 위해 처리군에 대응되는 대조군 선정 시 처리군의 일부가 탈락되는 경우가 발생할 수 있는데, 이로 인해 편의가 발생할 수 있다. 최근, Austin (2017)의 연구에서 이중 성향점수 보정(double propensity score adjustment)방법을 사용하는 것이 이에 대한 해결책이 될 수 있음을 제시하였다. 하지만, 처리효과 추정치의 표준오차는 이론적 추정치가 제시되지 않아 추정에 어려움이 있다. 본 연구에서는 이중 성향점수 보정 방법을 이용한 처리효과 추정치의 표준오차 추정을 위하여 두 가지 붓스트랩 방법을 제안한다. 첫 번째는 원 자료에서 성향점수 매칭 후 매칭 된 표본에서 붓스트랩 표본을 얻는 방법(simple 붓스트랩)이고, 두 번째는 원 자료에서 붓스트랩을 먼저 시행하고 각 붓 스트랩 표본에서 성향점수 매칭을 하는 방법(complex 붓스트랩)이다. 두 방법의 성능을 비교하기 위하여 다양한 상황을 가정하여 모의실험을 시행한 결과 complex 붓스트랩 방법이 경험적 표준오차와 더 가까운 값으로 추정함을 알 수 있었다. 95% 신뢰구간의 포함확률도 complex 방법을 사용했을 때 0.95에 훨씬 가까웠다. 실제 자료에 적용하였을 때에도 simple 방법은 complex 방법에 비해 표준오차를 작게 추정하였다.

• Communications for Statistical Applications and Methods
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• 제30권3호
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• pp.273-289
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• 2023

In this paper, we develop a new time series model for predicting IPO (initial public offering) data with non-negative integer value. The proposed model is based on integer-valued autoregressive (INAR) model with a Poisson thinning operator. Just as the heterogeneous autoregressive (HAR) model with daily, weekly and monthly averages in a form of cascade, the integer-valued heterogeneous autoregressive (INHAR) model is considered to reflect efficiently the long memory. The parameters of the INHAR model are estimated using the conditional least squares estimate and Yule-Walker estimate. Through simulations, bias and standard error are calculated to compare the performance of the estimates. Effects of model fitting to the Korea's IPO are evaluated using performance measures such as mean square error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) etc. The results show that INHAR model provides better performance than traditional INAR model. The empirical analysis of the Korea's IPO indicates that our proposed model is efficient in forecasting monthly IPO volumes.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.19-24
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• 2004

A number of creep data were collected and filed for type 316LN stainless steels through literature survey and experimental data produced in KAERI. Using these data, polynomial equations for predicting creep life were obtained for Larson Miller (L-M), Qrr-Sherby-Dorn (O-S-D) and Manson-Haferd (M-H) parametric methods. In order to find out the suitability for them, the relative standard error (RSE) and standard error of estimate (SEE) values were obtained by statistical process of creep data. The O-S-D parameter showed better fitting to creep-rupture data than the L-M or the M-H parameters, and the three parametric methods did not generate the large difference in the SEE and the RSE values.

• 지구물리
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• 제5권2호
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• pp.131-142
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• 2002

일반적으로 지자기 전달함수는 관측치와 예측치의 차이를 최소화하는 관점에서 해가 추정된다. 오차의 구조가 가우스 분포를 따르면 최소자승 추정이 최적의 추정이지만, 그렇지 않은 경우 전달 함수 추정을 심각하게 왜곡시킬 수 있으므로 오차 구조에 대한 정보가 요구된다. 본 연구에서는 Q-Q plot을 이용한 오차 구조으 검증을 통하여 실제 오차 구조에 대한 정보를 획득하였고 가우스 분포 가정을 벗어나는 오차 구조에 대해 외치(outlier)에 의한 영향을 최소로 하며 해를 추정하는 로버스트 추정(regression M-estimate)을 적용하였다. 오차가 가우스 분포를 따르는 경우, 최소자승 추정과 로버스트 추정은 유사한 결과를 나타내나, 오차가 가우스 분포를 벗어나는 경우 로버스트 추정이 최소자승 추정보다 부드러운 결과를 나타냄을 확인하였다.

• Journal of the Korean Statistical Society
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• 제26권3호
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• pp.383-395
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• 1997

This paper proves that the standard bootstrap approximation for the least absolute deviation (LAD) estimate of .beta. in AR(1) processes with infinite variance error terms is asymptotically valid in probability when the bootstrap resample size is much smaller than the original sample size. The theoretical validity results are supported by simulation studies.

• 한국정밀공학회지
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• 제19권9호
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• pp.51-58
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• 2002

Vehicle dynamics control systems are. complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed is good results in normal conditions. But the estimation error in severe braking is discontented. In this paper, we estimate the absolute vehicle speed by using the wheel speed data from standard 50-tooth anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used. Ten algorithms are verified experimentally to estimate the absolute vehicle speed and one of those is perfectly shown to estimate the vehicle speed with a 4% error during a braking maneuver.