This book develops the theory of statistical inference in statistical models with an infinite-dimensional parameter space, including mathematical foundations and key decision-theoretic principles.
Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.
This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.
This book serves well as an introduction into the more theoretical aspects of the use of spline models.
This book sets out to lay some of the foundations for subsampling methodology and related methods.
Casella, George, and Berger, Roger L. 2002. Statistical Inference. Duxbury. ... Deng, Li, Seltzer, Michael L., Yu, Dong, Acero, Alex, Mohamed, Abdel-rahman, and Hinton, Geoffrey E. 2010. ... Eckart, Carl, and Young, Gale. 1936.
Pages 153–180 of: Fayyad, U., Piatesky-Shapiro, G., Smyth, P., and Uthurusamy, R. (eds.), Advances in Knowledge Discovery and Data Mining. AAAI Press. 77 Chen, J., and Tan, X. 2009. Inference for multivariate normal mixtures.
This new edition has been revised and updated and in this fourth printing, errors have been ironed out.
Describes the interplay between the probabilistic structure (independence) and a variety of tools ranging from functional inequalities to transportation arguments to information theory.
PROBABILISTIC MATHEMATICS Editorial Board Z. Ghahramani (Department of Engineering, University of Cambridge) R. Gill (Mathematical ... Shurong Zheng and Zhidong Bai Mathematical Foundations of Infinite-Dimensional Statistical Models, ...
Praise for the first edition: "[This book] succeeds singularly at providing a structured introduction to this active field of research. ... it is arguably the most accessible overview yet published of the mathematical ideas and principles ...