Identifying subgroups of age and cohort effects in obesity prevalence

DOI10.1002/BIMJ.201900287zbMath1523.62168OpenAlexW3082472442WikidataQ99637946 ScholiaQ99637946MaRDI QIDQ6067318

No author found.

Publication date: 16 November 2023

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201900287

zbMATH Keywords

clusteringsubgroup analysisobesityage-period-cohortCBD-O

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Nearly unbiased variable selection under minimax concave penalty
• Clusterwise linear regression modeling with soft scale constraints
• Cluster analysis of longitudinal profiles with subgroups
• The generalized linear mixed cluster-weighted model
• Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model
• A spatial scan statistic
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• On fitting generalized linear and non-linear models of mortality
• Smoothing constrained generalized linear models with an application to the Lee-Carter model
• A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States
• Redefining standards for body mass index of the US population based on BRFSS data using mixtures
• Weighted Normal Spatial Scan Statistic for Heterogeneous Population Data
• Predictive Modeling of Obesity Prevalence for the U.S. Population
• Determining the number of components in mixtures of linear models.

This page was built for publication: Identifying subgroups of age and cohort effects in obesity prevalence