http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
The goal of this text is to provide the reader with a single book where they can find a brief account of many, modern topics in nonparametric inference. The book is aimed at Master's level or Ph.D. level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. This text covers a wide range of topics including: the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book has a mixture of methods and theory. Larry Wasserman is Professor of Statistics at Carnegie Mellon University and a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, multiple testing, and applications to astrophysics, bioinformatics and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathématiques de Montreal-Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics. He is the author of All of Statistics: A Concise Course in Statistical Inference (Springer, 2003).
Estimating the CDF and Statistical Functionals -- The Bootstrap and the Jackknife -- Smoothing: General Concepts -- Nonparametric Regression -- Density Estimation -- Normal Means and Minimax Theory -- Nonparametric Inference Using Orthogonal Functions -- Wavelets and Other Adaptive Methods -- Other Topics.
Artificial Intelligence (incl. Robotics)
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences