Asymptotic optimal inference for multivariate branching-Markov processes via martingale estimating functions and mixed normality

DOI10.1016/j.jmva.2011.02.002zbMath1274.62547OpenAlexW2036983722MaRDI QIDQ538181

Sun Young Hwang, Ishwar V. Basawa

Publication date: 23 May 2011

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2011.02.002

zbMATH Keywords

asymptotic optimalitylocal asymptotic mixed normalitymartingale estimating functionsbranching-Markov processlarge sample tests

Mathematics Subject Classification ID

Martingales with discrete parameter (60G42) Central limit and other weak theorems (60F05) Markov processes: estimation; hidden Markov models (62M05) Branching processes (Galton-Watson, birth-and-death, etc.) (60J80) Markov processes: hypothesis testing (62M02)

Related Items (9)

Branching Process Models to Identify Risk Factors for Infectious Disease Transmission ⋮ Non-stationary quasi-likelihood and asymptotic optimality ⋮ Asymmetry tests for bifurcating auto-regressive processes with missing data ⋮ Asymptotics for a class of generalized multicast autoregressive processes ⋮ Quasilikelihood and quasi-maximum likelihood for GARCH-type processes: estimating function approach ⋮ Martingale Estimating Functions for Stochastic Processes: A Review Toward a Unifying Tool ⋮ Non-ergodic martingale estimating functions and related asymptotics ⋮ The asymptotic behaviors for least square estimation of multi-casting autoregressive processes ⋮ A note on the local asymptotic mixed normality of a controlled branching process with a random control function

Uses Software

• FinTS

Cites Work

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