 Generalized partially linear models with missing covariatesHua Liang Journal of Multivariate Analysis, 2008, vol. 99, issue 5, 880-895 Abstract: In this article we study a semiparametric generalized partially linear model when the covariates are missing at random. We propose combining local linear regression with the local quasilikelihood technique and weighted estimating equation to estimate the parameters and nonparameters when the missing probability is known or unknown. We establish normality of the estimators of the parameter and asymptotic expansion for the estimators of the nonparametric part. We apply the proposed models and methods to a study of the relation between virologic and immunologic responses in AIDS clinical trials, in which virologic response is classified into binary variables. We also give simulation results to illustrate our approach. Keywords: AIDS; clinical; trial; Completely; missing; at; random; Local; linear; Local; quasilikelihood; Missing; at; random; Nonignorable; Penalized; quasilikelihood; Weighted; estimating; equation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:99:y:2008:i:5:p:880-895 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Multivariate Analysis is currently edited by de Leeuw, J. More articles in Journal of Multivariate Analysis from Elsevier |
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