Summary and Info
''The goal of this text is to provide readers 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.
More About the Author
Larry A. Wasserman is a Canadian statistician and a professor in the Department of Statistics and the Machine Learning Department at Carnegie Mellon University.
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