Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1152639 | Statistics & Probability Letters | 2014 | 10 Pages |
Abstract
By using a large deviation theory of the stochastic process and the moment information of errors, some large deviation results for the least squares estimator θnθn in a nonlinear regression model are obtained when errors satisfy some general conditions. For some p>1p>1, examples are presented to show that our results can be used in the case for supn≥1E|ξn|p=∞supn≥1E|ξn|p=∞ and a better bound can be obtained in the case for supn≥1E|ξn|p<∞supn≥1E|ξn|p<∞. Our results generalize and improve the corresponding ones.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Wenzhi Yang, Shuhe Hu,