A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance

DOI10.1016/J.CSDA.2013.01.024zbMath1471.62097OpenAlexW1976361004MaRDI QIDQ1621279

Pierre-Alexandre Beloeil, Geert Verbeke, Stijn Jaspers, Marc Aerts

Publication date: 8 November 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2013.01.024

zbMATH Keywords

censoringsemiparametricantimicrobial resistancepenalized mixture approach

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05)

Related Items (3)

Editorial: The 2nd special issue on advances in mixture models ⋮ A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance ⋮ Fitting Censored and Truncated Regression Data Using the Mixture of Experts Models

Uses Software

• glmmAK
• bootstrap

Cites Work

• Unnamed Item
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• A new semi-parametric mixture model for interval censored data, with applications in the field of antimicrobial resistance
• EM algorithms for multivariate Gaussian mixture models with truncated and censored data
• Density estimation and comparison with a penalized mixture approach
• Smooth Random Effects Distribution in a Linear Mixed Model
• Fitting Mixture Models to Grouped and Truncated Data via the EM Algorithm
• A Semi‐Parametric Shared Parameter Model to Handle Nonmonotone Nonignorable Missingness
• Model Selection and Multimodel Inference
• A new look at the statistical model identification

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