• Title/Summary/Keyword: Censored regression models

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THE STRONG CONSISTENCY OF THE ASYMMETRIC LEAST SQUARES ESTIMATORS IN NONLINEAR CENSORED REGRESSION MODELS

  • Choi, Seung-Hoe;Kim, Hae-Kyung
    • Communications of the Korean Mathematical Society
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    • v.18 no.4
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    • pp.703-712
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    • 2003
  • This paper deals with the strong consistency of the asymmetric least squares for the nonlinear censored regression models which includes dependent variables cut off midway by any of external conditions, and provide the sufficient conditions which ensure the strong consistency of proposed estimators of the censored regression models. One example is given to illustrate the application of the main result.

The Strong Consistency of Regression Quantiles Estimators in Nonlinear Censored Regression Models

  • Choi, Seung-Hoe
    • Journal of the Korean Data and Information Science Society
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    • v.13 no.1
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    • pp.157-164
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    • 2002
  • In this paper, we consider the strong consistency of the regression quantiles estimators for the nonlinear regression models when dependent variables are subject to censoring, and provide the sufficient conditions which ensure the strong consistency of proposed estimators of the censored regression models. one example is given to illustrate the application of the main result.

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The strong consistency of the $L_1$-norm estimators in censored nonlinear regression models

  • Park, Seung-Hoe;Kim, Hae-Kyung
    • Bulletin of the Korean Mathematical Society
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    • v.34 no.4
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    • pp.573-581
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    • 1997
  • This paper is concerned with the strong consistency of the $L_1$-norm estimators for the nonlinear regression models when dependent variables are subject to censoring, and provides the sufficient conditions which ensure the strong consistency of $L_1$-norm estimators of the censored regression models.

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Additive Regression Models for Censored Data (중도절단된 자료에 대한 가법회귀모형)

  • Kim, Chul-Ki
    • Journal of Korean Society for Quality Management
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    • v.24 no.1
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    • pp.32-43
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    • 1996
  • In this paper we develop nonparametric methods for regression analysis when the response variable is subject to censoring that arises naturally in quality engineering. This development is based on a general missing information principle that enables us to apply, via an iterative scheme, nonparametric regression techniques for complete data to iteratively reconstructed data from a given sample with censored observations. In particular, additive regression models are extended to right-censored data. This nonparametric regression method is applied to a simulated data set and the estimated smooth functions provide insights into the relationship between failure time and explanatory variables in the data.

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REGRESSION WITH CENSORED DATA BY LEAST SQUARES SUPPORT VECTOR MACHINE

  • Kim, Dae-Hak;Shim, Joo-Yong;Oh, Kwang-Sik
    • Journal of the Korean Statistical Society
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    • v.33 no.1
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    • pp.25-34
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    • 2004
  • In this paper we propose a prediction method on the regression model with randomly censored observations of the training data set. The least squares support vector machine regression is applied for the regression function prediction by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed prediction method.

Quasi-Likelihood Approach for Linear Models with Censored Data

  • Ha, Il-Do;Cho, Geon-Ho
    • Journal of the Korean Data and Information Science Society
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    • v.9 no.2
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    • pp.219-225
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    • 1998
  • The parameters in linear models with censored normal responses are usually estimated by the iterative maximum likelihood and least square methods. However, the iterative least square method is simple but hardly has theoretical justification, and the iterative maximum likelihood estimating equations are complicatedly derived. In this paper, we justify these methods via Wedderburn (1974)'s quasi-likelihood approach. This provides an explicit justification for the iterative least square method and also directly the iterative maximum likelihood method for estimating the regression coefficients.

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Estimating Methods on Exponential Regression Models with Censored Data

  • Ha, Il-Do;Lee, Youngjo;Song, Jae-Kee
    • Journal of the Korean Statistical Society
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    • v.28 no.2
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    • pp.195-210
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    • 1999
  • We consider a large class of exponential regression models with censored data and propose two modified Fisher scoring methods with corresponding algorithms. These proposed methods improve the Newton-Raphson method in estimating the model parameters. The simulated and real examples are illustrated in aspect of convergence.

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Censored varying coefficient regression model using Buckley-James method

  • Shim, Jooyong;Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.5
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    • pp.1167-1177
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    • 2017
  • The censored regression using the pseudo-response variable proposed by Buckley and James has been one of the most well-known models. Recently, the varying coefficient regression model has received a great deal of attention as an important tool for modeling. In this paper we propose a censored varying coefficient regression model using Buckley-James method to consider situations where the regression coefficients of the model are not constant but change as the smoothing variables change. By using the formulation of least squares support vector machine (LS-SVM), the coefficient estimators of the proposed model can be easily obtained from simple linear equations. Furthermore, a generalized cross validation function can be easily derived. In this paper, we evaluated the proposed method and demonstrated the adequacy through simulate data sets and real data sets.

Further Applications of Johnson's SU-normal Distribution to Various Regression Models

  • Choi, Pilsun;Min, In-Sik
    • Communications for Statistical Applications and Methods
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    • v.15 no.2
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    • pp.161-171
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    • 2008
  • This study discusses Johnson's $S_U$-normal distribution capturing a wide range of non-normality in various regression models. We provide the likelihood inference using Johnson's $S_U$-normal distribution, and propose a likelihood ratio (LR) test for normality. We also apply the $S_U$-normal distribution to the binary and censored regression models. Monte Carlo simulations are used to show that the LR test using the $S_U$-normal distribution can be served as a model specification test for normal error distribution, and that the $S_U$-normal maximum likelihood (ML) estimators tend to yield more reliable marginal effect estimates in the binary and censored model when the error distributions are non-normal.