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Efficient Estimation of the Partly Linear Additive Hazards Model with Current Status Data
dc.contributor.authorLu, Xuewenen_US
dc.contributor.authorSong, Peter X.‐k.en_US
dc.date.accessioned2015-03-05T18:24:42Z
dc.date.available2016-05-10T20:26:28Zen
dc.date.issued2015-03en_US
dc.identifier.citationLu, Xuewen; Song, Peter X.‐k. (2015). "Efficient Estimation of the Partly Linear Additive Hazards Model with Current Status Data." Scandinavian Journal of Statistics 42(1): 306-328.en_US
dc.identifier.issn0303-6898en_US
dc.identifier.issn1467-9469en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/110752
dc.description.abstractThis paper focuses on efficient estimation, optimal rates of convergence and effective algorithms in the partly linear additive hazards regression model with current status data. We use polynomial splines to estimate both cumulative baseline hazard function with monotonicity constraint and nonparametric regression functions with no such constraint. We propose a simultaneous sieve maximum likelihood estimation for regression parameters and nuisance parameters and show that the resultant estimator of regression parameter vector is asymptotically normal and achieves the semiparametric information bound. In addition, we show that rates of convergence for the estimators of nonparametric functions are optimal. We implement the proposed estimation through a backfitting algorithm on generalized linear models. We conduct simulation studies to examine the finite‐sample performance of the proposed estimation method and present an analysis of renal function recovery data for illustration.en_US
dc.publisherThe Johns Hopkins University Pressen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.othersemiparametric information bounden_US
dc.subject.othersieve methoden_US
dc.subject.othersplinesen_US
dc.subject.otherrate of convergenceen_US
dc.subject.otherbackfittingen_US
dc.titleEfficient Estimation of the Partly Linear Additive Hazards Model with Current Status Dataen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelStatistics (Mathematical)en_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/110752/1/sjos12108.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/110752/2/sjos12108-sup-0001-supinfo.pdf
dc.identifier.doi10.1111/sjos.12108en_US
dc.identifier.sourceScandinavian Journal of Statisticsen_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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