Let X1, . . . ,Xn be i.i.d. random observations. Let S = L + T be a U -statistic of order k ≥ 2, where L is a linear statistic having asymptotic normal distribution, and T is a stochastically smaller statistic. We show that the rate of convergence to normality for S can be simply expressed as the rate of convergence to normality for the linear part L plus a correction term, (varT) ln(varT), und...

The appearance of 64-bit processors allows a new approach t o microkernel desagn From our experience wi th a message passang microkernel MESHIX, we discovered that a multi-address space, POSIX architecture is unsvttable for general parallel applications development Angel was therefore deszgned to provide a more flexible envtronmeni. Central t o these arms is a simplzficataon of resources. This ...

Et (st) The mean, over training sets, of st. The subscript t in Et is redundant, since the only source of variability for st is the training sets, and it is included for emphasis. Vart (st) The variance, over training sets, of st ŝt An estimator of st. This estimator can be a function of only the training set t, i.e., ŝt = ŝt(t) (Chapters 2–5). It can also be a function of both the training and...