[Statlist] Friday, June 16, 2017 with Kim Hendrickx (KU Leuven)

[Maurer Letizia]

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ETH and University of Zurich

Organisers:

Proff. P. Bühlmann - L. Held - T. Hothorn - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf
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We are glad to announce the following talk:

Friday, June 16, 2017 at 15:15h  ETH Zurich HG G 19.141
with Kim Hendrickx (KU Leuven)
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Title:

current status linear regression <https://www.math.ethz.ch/sfs/news-and-events/research-seminar.html?s=fs17#e_10541>


Abstract:

In statistics one often has to find a method for analyzing data which are only indirectly given. One such situation is when one has ``current status data", which only give the information that a certain event has taken place or on the other hand still did not happen. So one observes the ``current status" of the matter. We consider a simple linear regression model where the dependent variable is not observed due to current status censoring and where no assumptions are made on the distribution of the unobserved random error terms. For this model, the theoretical performance of the maximum likelihood estimator (MLE), maximizing the likelihood of the data over all possible distribution functions and all possible regression parameters, is still an open problem. We construct $\sqrt{n}$-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 abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar

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