Improved estimation in a non-Gaussian parametric regression
Statistics Theory
2019-09-17 v1 Statistics Theory
Abstract
The paper considers the problem of estimating the parameters in a continuous time regression model with a non-Gaussian noise of pulse type. The noise is specified by the Ornstein-Uhlenbeck process driven by the mixture of a Brownian motion and a compound Poisson process. Improved estimates for the unknown regression parameters, based on a special modification of the James-Stein procedure with smaller quadratic risk than the usual least squares estimates, are proposed. The developed estimation scheme is applied for the improved parameter estimation in the discrete time regression with the autoregressive noise depending on unknown nuisance parameters.
Cite
@article{arxiv.1109.6493,
title = {Improved estimation in a non-Gaussian parametric regression},
author = {Evgeny Pchelintsev},
journal= {arXiv preprint arXiv:1109.6493},
year = {2019}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1105.5036