The use of approximating models in Monte Carlo maximum likelihood estimation.

DOI10.1016/S0167-7152(99)00074-7zbMath1070.62512OpenAlexW2076027324MaRDI QIDQ1808687

Anthony Y. C. Kuk

Publication date: 1999

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(99)00074-7

zbMATH Keywords

Monte Carlo simulationImportance samplingRandom effects modelMarginal likelihoodConjugate latent processGeneralised linear mixed model

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Monte Carlo methods (65C05)

Related Items (3)

An alternative derivation of the Kalman filter using the quasi-likelihood method ⋮ Break Detection for a Class of Nonlinear Time Series Models ⋮ The dimension-wise quadrature estimation of dynamic latent variable models for count data

Cites Work

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• Marginal Likelihood from the Gibbs Output
• A regression model for time series of counts
• Estimation in generalized linear models with random effects
• Exponential Family State Space Models Based on a Conjugate Latent Process
• Monte Carlo maximum likelihood estimation for non-Gaussian state space models
• Maximum Likelihood Algorithms for Generalized Linear Mixed Models
• Monte Carlo EM Estimation for Time Series Models Involving Counts
• The simulation smoother for time series models

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