Regression estimation from an individual stable sequence

We consider univariate regression estimation from an individual (non-random) sequence (x(1),y(1)), (x(2),y(2)),..is an element of R x R, which is stable in the sense that for each interval A subset of or equal to R, (i) the limiting relative frequency of A under x(1),x(2),... is governed by an unknown probability distribution mu, and (ii) the limiting average of those y(i) with x(i) is an element of A is governed by an unknown regression function m(.). A computationally simple scheme for estimating m(.) is exhibited, and is shown to be L-2 consistent for stable sequences {(x(i),y(i))} such that {y(i)} is bounded and there is a known upper bound for the variation of m(.) on intervals of the form (- i, i], i greater than or equal to 1. Complementing this positive result, it is shown that there is no consistent estimation scheme for the family of stable sequences whose regression functions have finite variation, even under the restriction that x(i) is an element of [0, 1] and y(i) is binary-valued.