Recursive approximate maximum likelihood estimation for a class of counting process models

DOI10.1016/0047-259X(91)90099-NzbMath0739.62069MaRDI QIDQ1182759

Peter Spreij

Publication date: 28 June 1992

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

zbMATH Keywords

consistencyLyapunov functionsrecursive algorithmcounting processleast squares estimationintensityapproximations of maximum likelihood estimatorsweak convergence for martingales

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05) Non-Markovian processes: estimation (62M09) Martingales with continuous parameter (60G44) Sequential estimation (62L12)

Related Items (4)

On delay-dependent stability for vector nonlinear stochastic delay-difference equations with Volterra diffusion term ⋮ Semimartingale stochastic approximation procedure and recursive estimation ⋮ Recursive estimation procedures for one-dimensional parameter of statistical models associated with semimartingales ⋮ Almost sure asymptotic stability of drift-implicit \(\theta\)-methods for bilinear ordinary stochastic differential equations in \(\mathbb R^1\)

Cites Work

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• Convergence results for continuous-time adaptive stochastic filtering algorithms
• Recursive parameter estimation for counting processes with linear intensity
• Quasi-least-squares estimation in semimartingale regression models
• Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems

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