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A large deviation result for the least squares estimators in nonlinear regression

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  • Shuhe, Hu

Abstract

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).

Suggested Citation

  • Shuhe, Hu, 1993. "A large deviation result for the least squares estimators in nonlinear regression," Stochastic Processes and their Applications, Elsevier, vol. 47(2), pages 345-352, September.
    • Handle: RePEc:eee:spapps:v:47:y:1993:i:2:p:345-352
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