Bayesian Deconvolution Analysis of Pulsatile Hormone Concentration Profiles

DOI10.1111/1541-0420.00075zbMath1210.62171OpenAlexW1999321551WikidataQ79257886 ScholiaQ79257886MaRDI QIDQ3079165

Timothy D. Johnson

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2027.42/65620

zbMATH Keywords

Bayesian analysisMarkov chain Monte Carlobirth-death processmixture modelmodel choicedeconvolution analysis

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Applications of branching processes (60J85) Physiology (general) (92C30)

Related Items (5)

Bayesian deconvolution of oil well test data using Gaussian processes ⋮ Modeling of Hormone Secretion‐Generating Mechanisms with Splines: A Pseudo‐Likelihood Approach ⋮ Analysis of Pulsatile Hormone Concentration Profiles with Nonconstant Basal Concentration: A Bayesian Approach ⋮ Detecting Pulsatile Hormone Secretions Using Nonlinear Mixed Effects Partial Spline Models ⋮ A Bayesian Approach to Modeling Associations Between Pulsatile Hormones

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Bayesian analysis of mixture models with an unknown number of components\,--\,an alternative to reversible jump methods.
• State-space modelling of time series sampled from continuous processes with pulses
• A Non-Gaussian Model for Time Series with Pulses
• Dealing With Label Switching in Mixture Models
• Computational and Inferential Difficulties with Mixture Posterior Distributions
• A Semiparametric Bayesian Approach to the Random Effects Model

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