A large deviation result for the least squares estimators in nonlinear regression
We give a law of large deviations (LLD) for LS estimator [theta] in a nonlinear regression model with dependent errors, i.e., an exponential inequality for the probability of a large deviation of [theta] from the true [theta], the LLD is as nice as in Sieders and Dzhaparidze (1987) which has independent errors. This generalizes the results in Sieders and Dzhaparidze (1987) and Prakasa Rao (1984).
Year of publication: |
1993
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Authors: | Shuhe, Hu |
Published in: |
Stochastic Processes and their Applications. - Elsevier, ISSN 0304-4149. - Vol. 47.1993, 2, p. 345-352
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Publisher: |
Elsevier |
Keywords: | large deviation least squares nonlinear regression |
Saved in:
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