Inference in generalized additive mixed modelsby using smoothing splines

Lin, Xihong; Zhang, D.

Inference in generalized additive mixed modelsby using smoothing splines

dc.contributor.author	Lin, Xihong	en_US
dc.contributor.author	Zhang, D.	en_US
dc.date.accessioned	2010-06-01T22:16:58Z
dc.date.available	2010-06-01T22:16:58Z
dc.date.issued	1999-04	en_US
dc.identifier.citation	Lin, X . ; Zhang, D . (1999). "Inference in generalized additive mixed modelsby using smoothing splines." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61(2): 381-400. <http://hdl.handle.net/2027.42/75296>	en_US
dc.identifier.issn	1369-7412	en_US
dc.identifier.issn	1467-9868	en_US
dc.identifier.uri	https://hdl.handle.net/2027.42/75296
dc.format.extent	264079 bytes
dc.format.extent	3109 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	text/plain
dc.publisher	Blackwell Publishers Ltd.	en_US
dc.rights	1999 Royal Statistical Society	en_US
dc.subject.other	Correlated Data	en_US
dc.subject.other	Generalized Linear Mixed Models	en_US
dc.subject.other	Laplace Approximation	en_US
dc.subject.other	Marginal Quasi-likelihood	en_US
dc.subject.other	Nonparametric Regression	en_US
dc.subject.other	Penalized Quasi-likelihood	en_US
dc.subject.other	Smoothing Parameters	en_US
dc.subject.other	Variance Components	en_US
dc.title	Inference in generalized additive mixed modelsby using smoothing splines	en_US
dc.type	Article	en_US
dc.subject.hlbsecondlevel	Statistics and Numeric Data	en_US
dc.subject.hlbtoplevel	Science	en_US
dc.description.peerreviewed	Peer Reviewed	en_US
dc.contributor.affiliationum	University of Michigan, Ann Arbor, USA,	en_US
dc.contributor.affiliationother	North Carolina Street University, Raleigh, USA	en_US
dc.description.bitstreamurl	http://deepblue.lib.umich.edu/bitstream/2027.42/75296/1/1467-9868.00183.pdf
dc.identifier.doi	10.1111/1467-9868.00183	en_US
dc.identifier.source	Journal of the Royal Statistical Society: Series B (Statistical Methodology)	en_US
dc.owningcollname	Interdisciplinary and Peer-Reviewed

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