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Article: Root-n estimability of some missing data models

TitleRoot-n estimability of some missing data models
Authors
KeywordsInformation Operator
Missing Data Model
Primary
Root-N Estimability
Score Operator
Secondary
Issue Date2012
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmva
Citation
Journal of Multivariate Analysis, 2012, v. 106, p. 147-166 How to Cite?
AbstractIt is known that in many missing data models, for example, survival data models, some parameters are root- n estimable while the others are not. When they are, their limiting distributions are often Gaussian and easy to use. When they are not, their limiting distributions, if exists, are often non-Gaussian and difficult to evaluate. Thus it is important to have some preliminary assessments of the root- n estimability in these models. In this article, we study this problem for four missing data models: two-point interval censoring, double censoring, interval truncation, and a case-control genetic association model. For the first three models, we identify some parameters which are not root- n estimable. For some root- n estimable parameters, we derive the corresponding information bounds when they exist. Also, as the Cox regression model is commonly used for such data, we give asymptotic efficient information for these regression parameters. For the case-control genetic association model, we compute the asymptotic efficient information and relative efficiency, in relation to that of the full data, when only the case-control status data are available, as is often the case in practice. © 2011 Elsevier Inc.
Persistent Identifierhttp://hdl.handle.net/10722/221676
ISSN
2015 Impact Factor: 0.857
2015 SCImago Journal Rankings: 1.458
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYuan, A-
dc.contributor.authorXu, J-
dc.contributor.authorZheng, G-
dc.date.accessioned2015-12-04T15:29:00Z-
dc.date.available2015-12-04T15:29:00Z-
dc.date.issued2012-
dc.identifier.citationJournal of Multivariate Analysis, 2012, v. 106, p. 147-166-
dc.identifier.issn0047-259X-
dc.identifier.urihttp://hdl.handle.net/10722/221676-
dc.description.abstractIt is known that in many missing data models, for example, survival data models, some parameters are root- n estimable while the others are not. When they are, their limiting distributions are often Gaussian and easy to use. When they are not, their limiting distributions, if exists, are often non-Gaussian and difficult to evaluate. Thus it is important to have some preliminary assessments of the root- n estimability in these models. In this article, we study this problem for four missing data models: two-point interval censoring, double censoring, interval truncation, and a case-control genetic association model. For the first three models, we identify some parameters which are not root- n estimable. For some root- n estimable parameters, we derive the corresponding information bounds when they exist. Also, as the Cox regression model is commonly used for such data, we give asymptotic efficient information for these regression parameters. For the case-control genetic association model, we compute the asymptotic efficient information and relative efficiency, in relation to that of the full data, when only the case-control status data are available, as is often the case in practice. © 2011 Elsevier Inc.-
dc.languageeng-
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/jmva-
dc.relation.ispartofJournal of Multivariate Analysis-
dc.subjectInformation Operator-
dc.subjectMissing Data Model-
dc.subjectPrimary-
dc.subjectRoot-N Estimability-
dc.subjectScore Operator-
dc.subjectSecondary-
dc.titleRoot-n estimability of some missing data models-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.identifier.doi10.1016/j.jmva.2011.11.007-
dc.identifier.scopuseid_2-s2.0-84862788061-
dc.identifier.scopuseid_2-s2.0-84864379185-
dc.identifier.volume106-
dc.identifier.spage147-
dc.identifier.epage166-
dc.identifier.isiWOS:000300913400010-
dc.identifier.isiWOS:000288681800001-

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