A Poisson geometric process approach for predicting drop-out and committed first-time blood donors

DOI10.1080/02664763.2014.881781OpenAlexW1988849980MaRDI QIDQ5128658

Wai-Yin Wan, Jennifer So-Kuen Chan, Philip L. H. Yu

Publication date: 28 October 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2014.881781

zbMATH Keywords

Bayesian methodmixture modelPoisson count datageometric process modeltrend movement

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• WinBUGS
• BUGS

Cites Work

• Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions
• Statistical inference for geometric processes with gamma distributions
• Geometric processes and replacement problem
• Statistical inference for geometric processes with lognormal distribution.
• Estimating time series models for count data using efficient importance sampling
• On convergence of posterior distributions
• Observation-driven models for Poisson counts
• A note on the optimal replacement problem
• Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
• A New Approach for Handling Longitudinal Count Data with Zero‐Inflation and Overdispersion: Poisson Geometric Process Model
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications
• Negative binomial loglinear mixed models
• Fluctuation Theory of Recurrent Events
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