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|Title: ||Current status linear regression|
|Authors: ||Hendrickx, Kim|
|Issue Date: ||2017|
|Citation: ||ANNALS OF STATISTICS,,|
|Status: ||In Press|
|Abstract: ||We construct √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 non-monotone 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.|
|Notes: ||Groeneboom, P (reprint author), Delft Univ Technol, Delft Inst Appl Math, Mekelweg 4, NL-2628 CD Delft, Netherlands.
|ISI #: ||000436600900002|
|Type: ||Journal Contribution|
|Appears in Collections: ||Research publications|
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|Non peer-reviewed author version||1.26 MB||Adobe PDF|
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