Multi-level models can benefit from minimizing higher-order variations: an illustration using child malnutrition data

DOI10.1080/00949655.2018.1553242OpenAlexW2902367601WikidataQ128892449 ScholiaQ128892449MaRDI QIDQ5107380

Sabbir Rahman, Azizur Rahman, Sumonkanti Das, Ashraf Ahamed

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2018.1553242

zbMATH Keywords

random effects modelconditional AICcontextual variablechild anthropometric indicespopulation hierarchy

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• Unnamed Item
• MEMSS

Cites Work

• Sample surveys: inference and analysis
• Model assisted survey sampling
• Simulating the characteristics of populations at the small area level: new validation techniques for a spatial microsimulation model in Australia
• Small Area Estimation
• Small Area Estimation and Microsimulation Modeling
• On the behaviour of marginal and conditional AIC in linear mixed models
• The Role of Sampling Weights When Modeling Survey Data
• Small-Area Estimation Under Informative Probability Sampling of Areas and Within the Selected Areas
• Weighting for Unequal Selection Probabilities in Multilevel Models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Mixed-Effects Models in S and S-PLUS
• Small area housing stress estimation in Australia: Calculating confidence intervals for a spatial microsimulation model
• General Multi-Level Modeling with Sampling Weights
• Micro-Level Estimation of Poverty and Inequality
• Conditional Akaike information for mixed-effects models

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