Estimation of regression parameters with arbitrary noise

The problem of estimation of linear regression parameters with arbitrary (nonzero mean, correlated and even nonrandom) noise is considered. The essential assumption is that the inputs are random, centered and independent of the noise. Convergence and rates of convergence for stochastic approximation (standard and with averaging) and least squares estimators are investigated under these conditions. Applications and generalizations of the obtained results are discussed.