• Journal of the Korean Data and Information Science Society
• /
• 제19권4호
• /
• pp.1247-1253
• /
• 2008

In this paper, we propose a interval censored regression spline model with a variance function (non-constant variance that depends on a predictor). Simulation studies show our estimates from MCECM algorithm are consistent, but biased when the sample size is small because of boundary effects. Also, we examined how the distribution of $x_i$ affects the converging speed of these consistent estimates.

• Journal of the Korean Statistical Society
• /
• 제12권1호
• /
• pp.46-56
• /
• 1983

An algorithm based on EM procedure which finds maximum likelihood estimators in a nonlinear regression with censored data is proposed, and asymptotic properties of the estimator are investigated in detail. Some numerical examples are also given.

• Communications for Statistical Applications and Methods
• /
• 제7권1호
• /
• pp.23-36
• /
• 2000

This paper discusses the local influence approach to the Weibull regression model with censored data. Diagnostics for the Weibull regression model are proposed and developed when simultaneous perturbations of the response vector are allowed.

• Communications for Statistical Applications and Methods
• /
• 제6권3호
• /
• pp.791-800
• /
• 1999

Gross and Lai(1996) proposed a new approach for ordinary regression with left-truncated and right-censored (I.t.r.c) data. This paper shows how to apply nonparametric algorithms such as multivariate adaptive regression splines to 1.t.r.c data.

• Journal of the Korean Data and Information Science Society
• /
• 제23권2호
• /
• pp.393-401
• /
• 2012

This paper deals with the estimations of the least squares support vector regression when the responses are subject to randomly right censoring. The estimation is performed via two steps - the ordinary least squares support vector regression and the least squares support vector regression with censored data. We use the empirical fact that the estimated regression functions subject to randomly right censoring are close to the true regression functions than the observed failure times subject to randomly right censoring. The hyper-parameters of model which affect the performance of the proposed procedure are selected by a generalized cross validation function. Experimental results are then presented which indicate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
• /
• 제25권5호
• /
• pp.569-575
• /
• 2018

Pareto distribution is important to analyze data in actuarial sciences, reliability, finance, and climatology. In general, unknown parameters of the Pareto distribution are estimated based on the maximum likelihood method that may yield inadequate inference results for small sample sizes and high percent censored data. In this paper, a new approach based on the regression framework is proposed to estimate unknown parameters of the Pareto distribution under the progressive Type-II censoring scheme. The proposed method provides a new regression type estimator that employs the spacings of exponential progressive Type-II censored samples. In addition, the provided estimator is a consistent estimator with superior performance compared to maximum likelihood estimators in terms of the mean squared error and bias. The validity of the proposed method is assessed through Monte Carlo simulations and real data analysis.

• 한국산업정보학회논문지
• /
• 제4권2호
• /
• pp.39-43
• /
• 1999

임의중도절단자료(randomly censored data)를 갖는 일반화된 지수회귀모헝을 고려하여 이 모형에서 모수를 추정하는 수정된 피선 점수화(modified Fisher scoring)방법을 제안한다. 이를 위해 우도방정식(likelihood equations)이 유도되고 추정알고리즘(estimating algorithm)이 개발된다. 실제의 자료를 통해 제안된 방법을 예증한다.

• Journal of the Korean Data and Information Science Society
• /
• 제27권6호
• /
• pp.1653-1660
• /
• 2016

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.

• Communications for Statistical Applications and Methods
• /
• 제13권3호
• /
• pp.765-776
• /
• 2006

In this paper we propose an estimation method on the regression model with randomly censored observations of the training data set. The weighted least squares support vector machine regression is applied for the regression function estimation by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed estimation method.

• 응용통계연구
• /
• 제34권6호
• /
• pp.957-968
• /
• 2021

역중도절단확률가중(inverse censoring probability weighting, ICPW)은 생존분석에서 흔히 사용되는 방법이다. 중도절단 회귀모형과 같은 ICPW 방법의 응용에 있어서 중도절단 확률의 정확한 추정은 핵심적인 요소라고 할 수 있다. 본 논문에서는 중도절단 확률의 추정이 ICPW 기반 중도절단 회귀모형의 성능에 어떠한 영향을 주는지 모의실험을 통하여 알아보았다. 모의실험에서는 Kaplan-Meier 추정량, Cox 비례위험(proportional hazard) 모형 추정량, 그리고 국소 Kaplan-Meier 추정량 세 가지를 비교하였다. 국소 KM 추정량에 대해서는 차원의 저주를 피하기 위해 공변량의 차원축소 방법을 추가적으로 적용하였다. 차원축소 방법으로는 흔히 사용되는 주성분분석(principal component analysis, PCA)과 절단역회귀(sliced inverse regression)방법을 고려하였다. 그 결과 Cox 비례위험 추정량이 평균 및 중위수 중도절단 회귀모형 모두에서 중도절단 확률을 추정하는 데 가장 좋은 성능을 보여주었다.