Semiparametric model for the dichotomized functional outcome after stroke: the Northern Manhattan Study
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Publication:693281
DOI10.1016/j.csda.2012.02.001zbMath1252.62043OpenAlexW1989965673MaRDI QIDQ693281
Ralph L. Sacco, Mandip S. Dhamoon, Joshua Willey, Yeseon Park Moon, Huaihou Chen, Mitchell S. V. Elkind, Myunghee Cho Paik
Publication date: 7 December 2012
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2012.02.001
kernel methodgeneralized estimating equationsregression splinessemiparametric longitudinal data analysis
Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10)
Cites Work
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- Longitudinal data analysis using generalized linear models
- Profile likelihood inferences on semiparametric varying-coefficient partially linear models
- Akaike's Information Criterion in Generalized Estimating Equations
- Profile-kernel versus backfitting in the partially linear models for longitudinal/clustered data
- Efficient Estimation in Marginal Partially Linear Models for Longitudinal/Clustered Data Using Splines
- Marginal nonparametric kernel regression accounting for within-subject correlation
- Semiparametric Regression for Clustered Data Using Generalized Estimating Equations
- Marginal Longitudinal Nonparametric Regression
- Nonparametric Function Estimation for Clustered Data When the Predictor is Measured without/with Error
- Varying-coefficient models and basis function approximations for the analysis of repeated measurements
- Analysis of Longitudinal Data With Semiparametric Estimation of Covariance Function
- Local polynomial regression analysis of clustered data
- Nonparametric Regression Methods for Longitudinal Data Analysis
- New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis
- Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data
- Partial Linear Regression Models for Clustered Data