Asymptotic properties of nonlinear estimates in stochastic models with finite design space. (English) Zbl 1176.62020
Summary: Under the condition that the design space is finite, new sufficient conditions for the strong consistency and asymptotic normality of the least-squares estimator in nonlinear stochastic regression models are derived. Similar conditions are obtained for the maximum-likelihood estimator in Bernoulli-type experiments. Consequences for the sequential design of experiments are pointed out.
MSC:
| 62F12 | Asymptotic properties of parametric estimators |
| 62J02 | General nonlinear regression |
| 62L05 | Sequential statistical design |
References:
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