Slochastic multicompartmental systems. a counting process approach for parameter estimation(°)

DOI10.1080/07362998608809078zbMath0587.62160OpenAlexW2076895137MaRDI QIDQ3713438

Maria B. Chiarolla

Publication date: 1986

Published in: Stochastic Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07362998608809078

zbMATH Keywords

maximum likelihood estimatorsstochastic integralscounting processescompartmental systemsmartingale representationsGaussian martingaleestimation problem of intensity parametersSkorokhod space of matrix functions

Mathematics Subject Classification ID

Martingales with discrete parameter (60G42) Central limit and other weak theorems (60F05) Markov processes: estimation; hidden Markov models (62M05) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (2)

Nonparametric estimation of partially observed stochastic multicompartmental systems ⋮ Anonparametric inference procedure for an illness-death model

Cites Work

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• Some results on linear stochastic multicompartmental systems
• Point processes and queues. Martingale dynamics
• Nonparametric inference for a family of counting processes
• Multivariate point processes: predictable projection, Radon-Nikodym derivatives, representation of martingales
• Weak convergence of stochastic integrals related to counting processes
• Martingales on Jump Processes. I: Representation Results
• Martingales on Jump Processes. II: Applications
• Weak convergence of probability measures and random functions in the function space D[0,∞)

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