  The asymptotic normality of U-statistics has so far been proved for iid data
and under various mixing conditions such as absolute regularity, but not for
strong mixing. We use a coupling technique introduced in 1983 by Bradley to
prove a new generalized covariance inequality similar to Yoshihara's. It
follows from the Hoeffding-decomposition and this inequality that U-statistics
of strongly mixing observations converge to a normal limit if the kernel of the
U-statistic fulfills some moment and continuity conditions.
  The validity of the bootstrap for U-statistics has until now only been
established in the case of iid data (see Bickel and Freedman). For mixing data,
Politis and Romano proposed the circular block bootstrap, which leads to a
consistent estimation of the sample mean's distribution. We extend these
results to U-statistics of weakly dependent data and prove a CLT for the
circular block bootstrap version of U-statistics under absolute regularity and
strong mixing. We also calculate a rate of convergence for the bootstrap
variance estimator of a U-statistic and give some simulation results.
