Large deviation for a least squares estimator in a nonlinear regression model

Wenzhi Yang and Shuhe Hu

Statistics & Probability Letters, 2014, vol. 91, issue C, 135-144

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 θn in a nonlinear regression model are obtained when errors satisfy some general conditions. For some p>1, examples are presented to show that our results can be used in the case for supn≥1E|ξn|p=∞ and a better bound can be obtained in the case for supn≥1E|ξn|p<∞. Our results generalize and improve the corresponding ones.

Keywords: Large deviation; Least squares estimator; Nonlinear regression model; ρ̃-mixing random variables; AANA random variables (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.spl.2014.04.022

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