Fitting mixed models to messy longitudinal data: a case study involving estimation of post mortem intervals

DOI10.1214/17-BJPS382zbMath1414.62476OpenAlexW2909648159WikidataQ128595799 ScholiaQ128595799MaRDI QIDQ1729810

Talita Zerbini, Francisco M. M. Rocha, Carmen D. S. André, Julio da Motta Singer

Publication date: 28 February 2019

Published in: Brazilian Journal of Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.bjps/1547456491

zbMATH Keywords

computed tomographycalibrationlinear mixed modelsdiagnosticsresidual analysisautopsyhypostasispost-mortem interval

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Uses Software

• nlme
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Cites Work

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• Random-Effects Models for Longitudinal Data
• Extension of the Gauss-Markov theorem to include the estimation of random effects
• Mixed Models
• A note on conditional AIC for linear mixed-effects models
• Best Linear Unbiased Estimation and Prediction under a Selection Model
• Statistical Analysis of Stochastic Processes in Time
• Segmented mixed models with random changepoints: a maximum likelihood approach with application to treatment for depression study
• Conditional Akaike information for mixed-effects models
• Graphical Tools for Detecting Departures from Linear Mixed Model Assumptions and Some Remedial Measures

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