Let Y1,Y2,... be a stationary, ergodic sequence of non-negative random variables with heavy tails. Under mixing conditions, we derive logarithmic tail asymptotics for the distributions of the arithmetic mean. If not all moments of Y1 are finite, these logarithmic asymptotics amount to a weaker form of the Baum-Katz law. Roughly, the sum of i.i.d. heavy-tailed non-negative random variables has the same behaviour as the largest term in the sum, and this phenomenon persists for weakly dependent random variables. Under mixing conditions, the rate of convergence in the law of large numbers is, as in the i.i.d. case, determined by the tail of the distribution of Y1. There are many results which make these statements more precise. The paper describes a particularly simple way to carry over logarithmic tail asymptotics from the i.i.d. to the mixing case.
Baum-Katz law Rate of convergence in the strong law Mixing Absolutely regular sequence Regularly varying tail Semiexponential distributions [beta]-mixing
