Empirical Distributions and Processes
Empirical Distributions and Processes Selected Papers from a Meeting at Oberwolfach, March 28 – April 3, 1976 / [electronic resource] :
edited by Peter Gaenssler, Pál Révész.
- VII, 150 p. online resource.
- Lecture Notes in Mathematics, 566 0075-8434 ; .
- Lecture Notes in Mathematics, 566 .
Weak approximations of the empirical process when parameters are estimated -- On the Erdös-Rényi increments and the P. Lévy modulus of continuity of a kiefer process -- Kolmogorov-smirnov tests when parameters are estimated -- On uniform convergence of measures with applications to uniform convergence of empirical distributions -- An alternative approach to glivenko-cantelli theorems -- Weak convergence under contiguous alternatives of the empirical process when parameters are estimated: The Dk approach -- Almost sure invariance principles for empirical distribution functions of weakly dependent random variables -- Three theorems of multivariate empirical process -- Weak convergence to stable laws by means of a weak invariance principle -- A necessary condition for the convergence of the isotrope discrepancy -- Two examples concerning uniform convergence of measures w.r.t. balls in Banach spaces.
9783540375159
10.1007/BFb0096874 doi
Distribution (Probability theory.
Probability Theory and Stochastic Processes.
QA273.A1-274.9 QA274-274.9
519.2
Weak approximations of the empirical process when parameters are estimated -- On the Erdös-Rényi increments and the P. Lévy modulus of continuity of a kiefer process -- Kolmogorov-smirnov tests when parameters are estimated -- On uniform convergence of measures with applications to uniform convergence of empirical distributions -- An alternative approach to glivenko-cantelli theorems -- Weak convergence under contiguous alternatives of the empirical process when parameters are estimated: The Dk approach -- Almost sure invariance principles for empirical distribution functions of weakly dependent random variables -- Three theorems of multivariate empirical process -- Weak convergence to stable laws by means of a weak invariance principle -- A necessary condition for the convergence of the isotrope discrepancy -- Two examples concerning uniform convergence of measures w.r.t. balls in Banach spaces.
9783540375159
10.1007/BFb0096874 doi
Distribution (Probability theory.
Probability Theory and Stochastic Processes.
QA273.A1-274.9 QA274-274.9
519.2