Current Status Censoring Models

Smooth estimators and their asymptotic properties

Doctoral thesis (2011)

Authors

B.I. Witte

Contributors

P. Groeneboom (promotor)

G. Jongbloed (promotor)

Department

Statistics and Probability () (TU Delft)

Statistics Current status censoring Density estimation Kernel smoothing Histogram estimation Asymptotic properties Kullback-Leibler divergence

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Published Date

15-03-2011

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Statistics and Probability

Abstract

Statistics deals with answering questions based on collected data. In medical applications, the quantity of interest can often not be observed directly. They are censored. It is a challenge to answer the question as precisely as possible based on the incomplete data. The quantity of interest can be censored in many different ways. In HIV vaccine trials, censoring results in “current status continuous mark” data. This is a very specific type of censoring and the method of maximum likelihood that usually works very well does not work in this case. Hence, alternative methods are needed. In this thesis, different alternative methods are introduced and studied from different perspectives.

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