Covariance miss-specification and the local influence approach in sensitivity analyses of longitudinal data with drop-outs

DOI10.1016/j.csda.2007.03.027zbMath1445.62194OpenAlexW2033317193MaRDI QIDQ1020647

R. J. O'Hara Hines, W. G. S. Hines

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.03.027

zbMATH Keywords

missing at randommissing responseslocal perturbationsambiguous influence diagnosticsmissing-not-at-randomnon-ignorable drop-outs

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Diagnostics, and linear inference and regression (62J20)

Related Items (2)

Intra-cluster correlation structure in longitudinal data analysis: selection criteria and misspecification tests ⋮ Indices for covariance mis-specification in longitudinal data analysis with no missing responses and with MAR drop-outs

Cites Work

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• Effects of Variance‐Function Misspecification in Analysis of Longitudinal Data
• Unbiased Estimating Equations From Working Correlation Models for Irregularly Timed Repeated Measures
• Influence analysis to assess sensitivity of the dropout process.

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