On asymptotics related to classical inference in stochastic differential equations with random effects

Trisha Maitra and Sourabh Bhattacharya

Statistics & Probability Letters, 2016, vol. 110, issue C, 278-288

Abstract: Delattre et al. (2013) considered n independent stochastic differential equations (SDE’s), where in each case the drift term is associated with a random effect, the distribution of which depends upon unknown parameters. Assuming the independent and identical (iid) situation the authors provide independent proofs of weak consistency and asymptotic normality of the maximum likelihood estimators (MLE’s) of the hyper-parameters of their random effects parameters.

Keywords: Asymptotic normality; Burkholder–Davis–Gundy inequality; Itô isometry; Maximum likelihood estimator; Random effects; Stochastic differential equations (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1016/j.spl.2015.10.001

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