Merging multiple longitudinal studies with study-specific missing covariates: A joint estimating function approach
From MaRDI portal
Publication:2809517
DOI10.1111/biom.12356zbMath1419.62469OpenAlexW2130469729WikidataQ40716198 ScholiaQ40716198MaRDI QIDQ2809517
Peter X.-K. Song, Fei Wang, Lu Wang
Publication date: 30 May 2016
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/biom.12356
Related Items
Cites Work
- Parametric fractional imputation for missing data analysis
- Improving generalised estimating equations using quadratic inference functions
- Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
- Estimating the dimension of a model
- Semiparametric least squares (SLS) and weighted SLS estimation of single-index models
- Convergence rates and asymptotic normality for series estimators
- Selecting likelihood weights by cross-validation
- Quadratic inference function approach to merging longitudinal studies: validation and joint estimation
- Subsample Ignorable Likelihood for Regression Analysis with Missing Data
- Consistent Model Selection and Data-Driven Smooth Tests for Longitudinal Data in the Estimating Equations Approach
- Inference and missing data
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- The Asymptotic Variance of Semiparametric Estimators
- Estimation in a semiparametric model for longitudinal data with unspecified dependence structure
- Pattern-Mixture Models for Multivariate Incomplete Data
- The weighted likelihood
- Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators
- Semiparametric Mean–Covariance Regression Analysis for Longitudinal Data
- Estimation of Semiparametric Models when the Criterion Function Is Not Smooth
- Semiparametric Models for Missing Covariate and Response Data in Regression Models
- Measurement Error Models with Auxiliary Data
- Local Influence for Generalized Linear Models with Missing Covariates
