In this paper, we obtain sufficient conditions in terms of projective
criteria under which the partial sums of a stationary process with values in
H (a real and separable Hilbert space) admits an approximation,
in Lp(H), p>1, by a martingale with stationary
differences, and we then estimate the error of approximation in
Lp(H). The results are exploited to further
investigate the behavior of the partial sums. In particular we obtain new
projective conditions concerning the Marcinkiewicz-Zygmund theorem, the
moderate deviations principle and the rates in the central limit theorem in
terms of Wasserstein distances. The conditions are well suited for a large
variety of examples, including linear processes or various kinds of weak
dependent or mixing processes. In addition, our approach suits well to
investigate the quenched central limit theorem and its invariance principle via
martingale approximation, and allows us to show that they hold under the
so-called Maxwell-Woodroofe condition that is known to be optimal.Comment: Published in at http://dx.doi.org/10.1214/13-AOP856 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org