A transitional non-parametric maximum pseudo-likelihood estimator for disease mapping

DOI10.1016/S0167-9473(02)00189-5zbMath1429.62493OpenAlexW2164148022MaRDI QIDQ951834

Emanuela Dreassi, Annibale Biggeri, Corrado Lagazio, Dankmar Boehning

Publication date: 4 November 2008

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

Full work available at URL: https://doi.org/10.1016/s0167-9473(02)00189-5

zbMATH Keywords

empirical Bayesautoregressive modelsnonparametric maximum likelihooddisease mappingtransitional models

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05)

Related Items (3)

Empirical Bayes methods for disease mapping ⋮ A hierarchical Bayesian approach to multiple testing in disease mapping ⋮ Modelling risks in disease mapping

Uses Software

• DismapWin

Cites Work

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• The geometry of mixture likelihoods: A general theory
• Efficiency of the conditional score in a mixture setting
• Bayesian image restoration, with two applications in spatial statistics (with discussion)
• Markov Regression Models for Time Series: A Quasi-Likelihood Approach
• Estimation of Cancer Mortality Rates: A Bayesian Analysis of Small Frequencies
• Data Analysis Using Stein's Estimator and its Generalizations
• Nonparametric Maximum Likelihood Estimation of a Mixing Distribution
• Recent Developments in Computer-Assisted Analysis of Mixtures
• A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models
• Mapping Disease and Mortality Rates Using Empirical Bayes Estimators

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