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Ian W. McKeague
Researcher at Columbia University
Publications - 170
Citations - 6253
Ian W. McKeague is an academic researcher from Columbia University. The author has contributed to research in topics: Estimator & Nonparametric statistics. The author has an hindex of 42, co-authored 162 publications receiving 5615 citations. Previous affiliations of Ian W. McKeague include National Institutes of Health & Medical College of Wisconsin.
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Elevated maternal C-reactive protein and autism in a national birth cohort
Alan S. Brown,Andre Sourander,Susanna Hinkka-Yli-Salomäki,Ian W. McKeague,Jouko Sundvall,Heljä-Marja Surcel +5 more
TL;DR: It is suggested that maternal inflammation may have a significant role in autism, with possible implications for identifying preventive strategies and pathogenic mechanisms in autism and other neurodevelopmental disorders.
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A partly parametric additive risk model
Ian W. McKeague,Peter Sasieni +1 more
TL;DR: In this article, the authors proposed a model that takes the additive structure of Aalen's model and imposes parametric constraints to obtain a semiparametric submodel, which may be more appropriate in some applications.
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Extending the Scope of Empirical Likelihood
TL;DR: In this paper, the authors extend the scope of empirical likelihood methodology ill three directions: to allow for plug-in estimates of nuisance parameters in estimating equations, slower than root n-rates of convergence, and settings in which there are a relatively large number of estimating equations compared to the sample size.
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Folic acid supplements in pregnancy and severe language delay in children
Christine Roth,Per Magnus,Synnve Schjølberg,Camilla Stoltenberg,Pål Surén,Ian W. McKeague,George Davey Smith,Ted Reichborn-Kjennerud,Ezra Susser +8 more
TL;DR: Maternal use of folic acid supplements in early pregnancy was associated with a reduced risk of severe language delay in children at age 3 years, among this Norwegian cohort of mothers and children.
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Wavelet Methods for Curve Estimation
TL;DR: In this paper, it was shown that there is a fundamental instability in the asymptotic variance of wavelet estimators caused by the lack of translation invariance of the wavelet transform.