Semi-parametric Random Censorship Models

Gerhard Dikta ()
Additional contact information
Gerhard Dikta: Fachhochschule Aachen

Chapter Chapter 3 in From Statistics to Mathematical Finance, 2017, pp 43-56 from Springer

Abstract: Abstract Starting with an identifying Volterra type integral equation for the survival function under randomly right censored observations, we derive general product type estimators. These general estimators can be specified according to the additional information about the conditional expectation of the indicator given the observation time. Among others, the well-known nonparametric Kaplan-Meier and two semi-parametric estimators are derived. For the latter ones, the conditional expectation of the indicator has to be parameterizable. Some important probabilistic properties of these semi-parametric estimators are reviewed here together with their nonparametric counterparts. In particular, a strong law and asymptotic normality of semi-parametric integrals together with their efficiency are discussed. Furthermore, validation tests for the parametric assumption are considered and bootstrapping under these semi-parametric models is reviewed.

Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-50986-0_3

Ordering information: This item can be ordered from
http://www.springer.com/9783319509860

DOI: 10.1007/978-3-319-50986-0_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().