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CURRENT STATUS LINEAR REGRESSION
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DOI：10.1214/17-AOS1589.png)
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Abstract
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We construct root n-consistent and asymptotically normal estimates for the finite dimensional regression parameter in the current status linear regression model, which do not require any smoothing device and are based on maximum likelihood estimates (MLEs) of the infinite dimensional parameter. We also construct estimates, again only based on these MLEs, which are arbitrarily close to efficient estimates, if the generalized Fisher information is finite. This type of efficiency is also derived under minimal conditions for estimates based on smooth nonmonotone plug-in estimates of the distribution function. Algorithms for computing the estimates and for selecting the bandwidth of the smooth estimates with a bootstrap method are provided. The connection with results in the econometric literature is also pointed out.
Keywords:
Current status
linear regression
MLE
semiparametric model
Journal
IF:
3.7
Papers:
2.8K
Citations:
2.9W
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Cited Papers
ShareSaveShareSaveShareSaveShareSaveNONPARAMETRIC ANALYSIS OF A GENERALIZED REGRESSION-MODEL - THE MAXIMUM RANK CORRELATION ESTIMATOR
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ShareSaveEFFICIENCY BOUNDS FOR DISTRIBUTION-FREE ESTIMATORS OF THE BINARY CHOICE AND THE CENSORED REGRESSION-MODELS.png)
ECONOMETRICA
ECONOMETRICAIF7.1
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