• The Korean Journal of Applied Statistics
• /
• v.31 no.1
• /
• pp.123-137
• /
• 2018

A nonlinear mixed effects model is mainly used to analyze repeated measurement data in various fields. A nonlinear mixed effects model consists of two stages: the first-stage individual-level model considers intra-individual variation and the second-stage population model considers inter-individual variation. The individual-level model, which is the first stage of the nonlinear mixed effects model, estimates the parameters of the nonlinear regression model. It is the same as the general nonlinear regression model, and usually estimates parameters using the least squares estimation method. However, the least squares estimation method may have a problem that the estimated value of the parameters and standard errors become extremely large if the assumed nonlinear function is not explicitly revealed by the data. In this paper, a new estimation method is proposed to solve this problem by introducing the ridge regression method recently proposed in the nonlinear regression model into the first-stage individual-level model of the nonlinear mixed effects model. The performance of the proposed estimator is compared with the performance with the standard estimator through a simulation study. The proposed methodology is also illustrated using quantitative high throughput screening data obtained from the US National Toxicology Program.

• Journal of the Korean Data and Information Science Society
• /
• v.5 no.2
• /
• pp.29-44
• /
• 1994

본 연구는 누적위험률함수에 대한 베이지안 추정량을 이용하여 생존함수의 추정량을 제안하고, counting process 이론과 martingale 이론을 이용하여 대표본하에서 제안된 추정량의 일양적 일치성과 점근적 정규성을 밝힌다. 또한 모의실험을 통하여 추정량들의 효율성을 편의와 평균제곱오차의 측면에서 비교하고자 한다.

• The Korean Journal of Applied Statistics
• /
• v.11 no.1
• /
• pp.163-175
• /
• 1998

The change of hazard rates at some unknown time point has been the interest of many statisticians. But it was restricted to the constant hazard rates which correspond to the exponential distribution. In this paper we generalize the change-point model in which any specific functional forms of hazard rates are net assumed. The assumed model includes various types of changes before and after the unknown time point. The Nelson estimator of cumulative hazard function is introduced. We estimate the change-point maximizing slope changes of Nelson estimator. Consistency and asymptotic distribution of bootstrap estimator are obtained using the martingale theory. Through a Monte Carlo study we check the performance of the proposed method. We also explain the proposed method using the Stanford Heart Transplant Data set.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.1
• /
• pp.33-39
• /
• 2013

In the transformation of response variable in partial linear models outliers can cause a bad effect on estimating the transformation parameter, just as in the linear models. To solve this problem the processes of estimating transformation parameter and detecting outliers are needed, but have difficulties to be performed due to the arbitrariness of the nonparametric function included in the partial linear model. In this study, through the estimation of nonparametric function and outlier detection methods such as a sequential test and a maximum trimmed likelihood estimation, processes for transforming response variable robust to outliers in partial linear models are suggested. The proposed methods are verified and compared their effectiveness by simulation study and examples.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.4
• /
• pp.755-762
• /
• 2013

In this paper we consider the NHPP software reliability model. And we deal with the maximum likelihood estimation and the Bayesian estimation with conjugate prior for parameter inference in the mean value function of Goel-Okumoto model (1979). The parameter estimates for the proposed model is presented by MLE and Bayes estimator in data set. We compare the predicted number of faults with the actual data set using the proposed mean value function.

• Proceedings of the Korean Statistical Society Conference
• /
• 2005.05a
• /
• pp.251-255
• /
• 2005

중도절단된 자료와 표본수가 적은 자료를 가지는 생존분석에서 생존율을 추정하거나 두 집단의 생존율을 비교할 때 정규분포 근사를 가정한 신뢰구간을 이용하는 데는 많은 어려움이 생긴다. 생존함수의 신뢰구간에 대한 중도절단을, 표본의 크기에 따른 다양한 상황의 모의실험을 통하여 Kaplan-Meier, Nelson, 적률 추정량 그리고 cox model의 ${\beta}$을 가지고 붓스트랩을 이용한 신뢰구간과 비모수 신뢰구간, 우도비 신뢰구간의 실제 포함 확률을 비교해보고자 한다.

• The KIPS Transactions:PartD
• /
• v.10D no.5
• /
• pp.805-812
• /
• 2003

This paper presents a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process (NHPP) and performs Bayesian inference using prior information. The failure process is analyzed to develop a suitable mean value function for the NHPP; expressions are given for several performance measure. The parametric inferences of the model using Logarithmic Poisson model, Crow model and Rayleigh model is discussed. Bayesian computation and model selection using the sum of squared errors. The numerical results of this models are applied to real software failure data. Tools of parameter inference was used method of Gibbs sampling and Metropolis algorithm. The numerical example by T1 data (Musa) was illustrated.

• The Korean Journal of Applied Statistics
• /
• v.32 no.5
• /
• pp.703-720
• /
• 2019

The influence of temperature on electricity demand is increasing due to extreme weather and climate change, and the climate impacts involves nonlinearity, asymmetry and complexity. Considering changes in government energy policy and the development of the fourth industrial revolution, it is important to assess the climate effect more accurately for stable management of electricity supply and demand. This study aims to analyze the effect of temperature change on electricity demand using the partial linear model. The main results obtained using the time-unit high frequency data for meteorological variables and electricity consumption are as follows. Estimation results show that the relationship between temperature change and electricity demand involves complexity, nonlinearity and asymmetry, which reflects the nonlinear effect of extreme weather. The prediction accuracy of in-sample and out-of-sample electricity forecasting using the partial linear model evidences better predictive accuracy than the conventional model based on the heating and cooling degree days. Diebold-Mariano test confirms significance of the predictive accuracy of the partial linear model.

• The Korean Journal of Applied Statistics
• /
• v.30 no.5
• /
• pp.665-679
• /
• 2017

When measuring productive efficiency, often it is necessary to have knowledge of the production frontier function that shows the maximum possible output of production units as a function of inputs. Canonical parametric forms of the frontier function were initially considered under the framework of stochastic frontier model; however, several additional nonparametric methods have been developed over the last decade. Efforts have been recently made to impose shape constraints such as monotonicity and concavity on the non-parametric estimation of the frontier function; however, most existing methods along that direction suffer from unnecessary non-smooth points of the frontier function. In this paper, we propose methods to estimate the smooth frontier function with monotonicity for stochastic frontier models and investigate the effect of imposing a monotonicity constraint into the estimation of the frontier function and the finite dimensional parameters of the model. Simulation studies suggest that imposing the constraint provide better performance to estimate the frontier function, especially when the sample size is small or moderate. However, no apparent gain was observed concerning the estimation of the parameters of the error distribution regardless of sample size.

• Journal of Korean Society of Transportation
• /
• v.15 no.1
• /
• pp.85-103
• /
• 1997

국내 도시교통에서 도시고속도로가 차지하는 비중은 급증하고 있으나 이의 효율적 인 운영은 아직 초보수준인 실정이다. 도시고속도로의 운영전략이나 기하구조 설계대안을 개발·분석·평가하는데 시뮬레이션 모형을 활용하는 것은 필수적이나 외국에서 개발된 모형 을 국내에 적용하는 데에는 많은 제약이 따르고 있다. 따라서 본 연구는 국내 현실에 적합 한 도시고속도로 교통류 시뮬레이션 모형을 개발하려는데 그 목적이 있으며 연속 교통류 모 형의 개발, 모수추정 방법의 제시, 컴퓨터 코딩, 모형평가의 세부작업이 수행되었다. URFSIM-1은 각 구간에서 통행목적지별 차량 수를 추적할 수 있는 통행수요모형 기능에 구 간내 이동을 동적으로 기술할 수 있는 거시적 교통류 모형을 결합한 것을 기본 교통류 모형 으로 채택하고 있다. 비선형 최소 자승법에 의해 교통류 모형 모수와 O-D 모수를 추정하는 방법이 제시되었다. 마지막으로 유고상황을 가상한 정성분석과 미국 도시고속도로에서 수집 한 현장자료를 이용한 모형의 평가를 시행하였다.