  In this paper, we derive valid Edgeworth expansions for studentized versions
of a large class of statistics when the data are generated by a strongly mixing
process. Under dependence, the asymptotic variance of such a statistic is given
by an infinite series of lag-covariances, and therefore, studentizing factors
(i.e., estimators of the asymptotic standard error) typically involve an
increasing number, say, $\ell$ of lag-covariance estimators, which are
themselves quadratic functions of the observations. The unboundedness of the
dimension $\ell$ of these quadratic functions makes the derivation and the form
of the expansions nonstandard. It is shown that in contrast to the case of the
studentized means under independence, the derived Edgeworth expansion is a
superposition of three distinct series, respectively, given by one in powers of
$n^{-1/2}$, one in powers of $[n/\ell]^{-1/2}$ (resulting from the standard
error of the studentizing factor) and one in powers of the bias of the
studentizing factor, where $n$ denotes the sample size.
