  The paper is concerned with stochastic approximation procedures having three
main characteristics: truncations with random moving bounds, a matrix valued
random step-size sequence, and a dynamically changing random regression
function. We study convergence and rate of convergence. Main results are
supplemented with corollaries to establish various sets of sufficient
conditions, with the main emphases on the parametric statistical estimation.
The theory is illustrated by examples and special cases.
