Asymptotic properties of nonlinear estimates in stochastic models with finite design space

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 on the sequential design of experiments are pointed out.