Mixed domain asymptotics for a stochastic process model with time trend and measurement error
Abstract
We consider a stochastic process model with time trend and measurement error. We establish consistency and derive the limiting distributions of the maximum likelihood (ML) estimators of the covariance function parameters under a general asymptotic framework, including both the fixed domain and the increasing domain frameworks, even when the time trend model is misspecified or its complexity increases with the sample size. In particular, the convergence rates of the ML estimators are thoroughly characterized in terms of the growing rate of the domain and the degree of model misspecification/complexity.
Cite
@article{arxiv.1609.08898,
title = {Mixed domain asymptotics for a stochastic process model with time trend and measurement error},
author = {Chih-Hao Chang and Hsin-Cheng Huang and Ching-Kang Ing},
journal= {arXiv preprint arXiv:1609.08898},
year = {2016}
}
Comments
Published at http://dx.doi.org/10.3150/15-BEJ740 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)