High Dimensional Probability III

The title High Dimensional Probability is an attempt to describe the many trib utaries of research on Gaussian processes and probability in Banach spaces that started in the early 1970's. In each of these fields it is necessary to consider large classes of stochastic processes under minimal conditions. There are rewards in re search of this sort. One can often gain deep insights, even about familiar processes, by stripping away details that in hindsight turn out to be extraneous. Many of the problems that motivated researchers in the 1970's were solved. But the powerful new tools created for their solution, such as randomization, isoperimetry, concentration of measure, moment and exponential inequalities, chaining, series representations and decoupling turned out to be applicable to other important areas of probability. They led to significant advances in the study of empirical processes and other topics in theoretical statistics and to a new ap proach to the study of aspects of Levy processes and Markov processes in general. Papers on these topics as well as on the continuing study of Gaussian processes and probability in Banach are included in this volume.

I. Measures on General Spaces and Inequalities. - Stochastic inequalities and perfect independence. - Prokhorov-LeCam-Varadarajan s compactness criteria for vector measures on metric spaces. - On measures in locally convex spaces. - II. Gaussian Processes. - Karhunen-Loeve expansions for weighted Wiener processes and Brownian bridges via Bessel functions. - Extension du thèoréme de Cameron-Martin aux translations aleatoires. II. Intègrabilitè des densitès. - III. Limit Theorems. - Rates of convergence for Lèvy s modulus of continuity and Hinchin s law of the iterated logarithm. - On the limit set in the law of the iterated logarithm for U-statistics of order two. - Perturbation approach applied to the asymptotic study of random operators. - A uniform functional law of the logarithm for a local Gaussian process. - Strong limit theorems for mixing random variables with values in Hilbert space and their applications. - IV. Local Times. - Local time-space calculus and extensions of Ito s formula. - Local times on curves and surfaces. - V. Large, Small Deviations. - Large deviations of empirical processes. - Small deviation estimates for some additive processes. - VI. Density Estimation. - Convergence in distribution of self-normalized sup-norms of kernel density estimators. - Estimates of the rate of approximation in the CLT for L1-norm of density estimators. - VII. Statistics via Empirical Process Theory. - Statistical nearly universal Glivenko-Cantelli classes. - Smoothed empirical processes and the bootstrap. - A note on the asymptotic distribution of Berk-Jones type statistics under the null hypothesis. - A note on the smoothed bootstrap.