Robust Model-Based Inference for Incomplete Data via Penalized Spline Propensity Prediction

DOI10.1080/03610910802255840zbMath1286.62088OpenAlexW2074760807MaRDI QIDQ3543717

Hyonggin An, Roderick J. A. Little

Publication date: 4 December 2008

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910802255840

zbMATH Keywords

bootstrapasymptotic varianceGibbs samplermissing datamultiple imputationresponse propensitypenalized spline

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Robustness and adaptive procedures (parametric inference) (62F35) Estimation in survival analysis and censored data (62N02)

Related Items (2)

Robust Bayesian inference for big data: combining sensor-based records with traditional survey data ⋮ Comments on: Missing data methods in longitudinal studies: a review

Uses Software

• SemiPar

Cites Work

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• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Multiple Imputation After 18+ Years
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• Inference and missing data
• Nonparametric Estimation of Mean Functionals with Data Missing at Random
• Bootstrap for Imputed Survey Data
• Semiparametric Regression
• Penalized Spline Estimation for Partially Linear Single-Index Models
• Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse
• A Bootstrap Method for Using Imputation Techniques for Data With Missing Values

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