A distributed recursive least squares algorithm achieves almost sure convergence for infinite-dimensional stochastic regression models under a cooperative excitation condition without requiring independence or stationarity of regressors.
Asymptotic convergence of a distributed weighted least squares algorithm for networked systems with vector node variables,
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Distributed adaptive estimation for stochastic large regression models
A distributed recursive least squares algorithm achieves almost sure convergence for infinite-dimensional stochastic regression models under a cooperative excitation condition without requiring independence or stationarity of regressors.