Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/32440

TitleGoodness-of-fit tests for a semiparametric model under random double truncation
Author(s)Moreira, Carla
Uña-Álvarez, Jacobo de
Keilegom, Ingrid van
KeywordsBootstrap
Survival analysis
Truncated data
Issue dateOct-2014
PublisherSpringer
JournalComputational Statistics
Abstract(s)Doubly truncated data are commonly encountered in areas like medicine, astronomy, economics, among others. A semiparametric estimator of a doubly truncated random variable may be computed based on a parametric specification of the distribution function of the truncation times. This semiparametric estimator outperforms the nonparametric maximum likelihood estimator when the parametric information is correct, but might behave badly when the assumed parametric model is far off. In this paper we introduce several goodness-of-fit tests for the parametric model. The proposed tests are investigated through simulations. For illustration purposes, the tests are also applied to data on the induction time to acquired immune deficiency syndrome for blood transfusion patients.
TypeArticle
URIhttp://hdl.handle.net/1822/32440
DOI10.1007/s00180-014-0496-z
ISSN0943-4062
Publisher versionhttp://link.springer.com/journal/180
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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