On asymptotic inference in stochastic differential equations with time-varying covariates
Article Type
Research Article
Publication Title
Canadian Journal of Statistics
Abstract
In this article, we introduce a system of stochastic differential equations (SDEs) consisting of time-dependent covariates and consider both fixed and random effects. We also allow the functional part associated with the drift function to depend upon unknown parameters. For this general SDE system we establish consistency and asymptotic normality of the maximum likelihood estimator. We consider a Bayesian approach for learning about the population parameters, and prove consistency and asymptotic normality of the corresponding posterior distribution. We supplement our theoretical investigation with simulated and real data analyses, obtaining encouraging results in both cases. The Canadian Journal of Statistics 46: 635–655; 2018 © 2018 Statistical Society of Canada.
First Page
635
Last Page
655
DOI
10.1002/cjs.11471
Publication Date
12-1-2018
Recommended Citation
Maitra, Trisha and Bhattacharya, Sourabh, "On asymptotic inference in stochastic differential equations with time-varying covariates" (2018). Journal Articles. 1116.
https://digitalcommons.isical.ac.in/journal-articles/1116
Comments
All Open Access, Green