The ultimate objective of this paper is to develop new techniques that can be used for the analysis of performance degradation due to statistical uncertainty for a wide class of linear stochastic systems. For this we need new technical tools similar to those used in [ L. Gerencser, Statist. Plann. Inference, 41 ( 1994), pp. 303 - 325]. The immediate technical objective is to extend the previous technical results to the Djereveckii-Fradkov-Ljung scheme with enforced boundedness. Our starting point is a standard approximation of the estimation error used in the asymptotic theory of recursive estimation. Tight control of the difference between the estimation error and its standard approximation, referred to as residuals, is a crucial point in our applications. The main technical advance of the present paper is a set of strong approximation theorems for three closely related recursive estimation algorithms in which, for any q >= 1, the L(q)-norms of the residual terms are shown to tend to zero with rate N(-1/2-epsilon) with some epsilon > 0. This is a significant extension of previous results for the recursive prediction error or RPE estimator of ARMA processes given in [ L. Gerencser, Systems Control Lett., 21 ( 1993), pp. 347 - 351]. Two useful corollaries will be derived. In the first a standard transform of the estimation-error process for the basic recursive estimation method, Algorithm CR, will be shown to be L-mixing, while in the second the asymptotic covariance matrix of the estimator for the same method will be given. Applications to multivariable adaptive prediction and the minimum-variance self-tuning regulator for ARMAX systems will be described.