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

Hu Shuhe

Stochastic Processes and their Applications, 1993, vol. 47, issue 2, 345-352

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).

Keywords: large; deviation; least; squares; nonlinear; regression (search for similar items in EconPapers)
Date: 1993
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