Efficient semiparametric marginal estimation for longitudinal/clustered data Academic Article

abstract

• We consider marginal generalized semiparametric partially linear models for clustered data. Lin and Carroll derived the semiparametric efficient score function for this problem in the multivariate Gaussian case, but they were unable to construct a semiparametric efficient estimator that actually achieved the semiparametric information bound. Here we propose such an estimator and generalize the work to marginal generalized partially linear models. We investigate asymptotic relative efficiencies of the estimators that ignore the within-cluster correlation structure either in nonparametric curve estimation or throughout. We evaluate the finite-sample performance of these estimators through simulations and illustrate it using a longitudinal CD4 cell count dataset. Both theoretical and numerical results indicate that properly taking into account the within-subject correlation among the responses can substantially improve efficiency. 2005 American Statistical Association.

authors

• Carroll, Raymond

published proceedings

• JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

author list (cited authors)

• Wang, N., Carroll, R. J., & Lin, X. H.

citation count

• 135

complete list of authors

• Wang, N||Carroll, RJ||Lin, XH

publication date

• March 2005

publisher

• Taylor & Francis  Publisher

published in

• JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION  Journal

keywords

• Clustered Data
• Generalized Estimating Equations
• Kernel Method
• Longitudinal Data
• Marginal Models
• Nonparametric Regression
• Partially Linear Model
• Profile Method
• Sandwich Estimator
• Semiparametric Information Bound
• Semiparametric-efficient Score
• Time-dependent Covariate

Digital Object Identifier (DOI)

• 10.1198/016214504000000629

start page

• 147

end page

• 157

volume

• 100

issue

• 469

URL

• http%3A%2F%2Fdx.doi.org%2F10.1198%2F016214504000000629