Modified Semiparametric Maximum Likelihood Estimator in Linear Regression Analysis With Complete Data or Right-Censored Data

Consider a linear regression model where the response variable may be right-censored. The standard maximum likelihood estimator (MLE)-based parametric approach to estimation of regression coefficients requires that the parametric form of the error distribution be known. Given a dataset, we may not b...

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Bibliographic Details
Published in:Technometrics 2005-02, Vol.47 (1), p.34-42
Main Authors: Yu, Qiqing, Wong, George Y.C
Format: Article
Language:English
Subjects:
Algorithms
Arbitrary error distribution
Asymptotic normality
Censored data
Censorship
Covariance matrices
Datasets
Engineering
Estimators
Exact sciences and technology
Linear inference, regression
Linear regression
Mathematics
Maximum likelihood estimation
Maximum likelihood estimators
Noniterative algorithm
Normal distribution
Probability and statistics
Random variables
Regression analysis
Sample size
Sciences and techniques of general use
Semiparametric MLE
Shear strength
Simulation
Simulation studies
Statistical methods
Statistics
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Summary:Consider a linear regression model where the response variable may be right-censored. The standard maximum likelihood estimator (MLE)-based parametric approach to estimation of regression coefficients requires that the parametric form of the error distribution be known. Given a dataset, we may not be able to find a valid parametric form for the error distribution. In such a case the error distribution is unknown and arbitrary, and a semiparametric approach is plausible. A special modified semiparametric MLE (MSMLE) of the regression coefficients is proposed. Simulation suggests that the MSMLE is consistent is asymptotically normally distributed and may be efficient. The new procedure is applied to engineering data.
ISSN:0040-1706
1537-2723
DOI:10.1198/004017004000000554