Nonparametric Inference on Manifolds - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonparametric Inference on Manifolds write by Abhishek Bhattacharya. This book was released on 2012-04-05. Nonparametric Inference on Manifolds available in PDF, EPUB and Kindle. Ideal for statisticians, this book will also interest probabilists, mathematicians, computer scientists, and morphometricians with mathematical training. It presents a systematic introduction to a general nonparametric theory of statistics on manifolds, with emphasis on manifolds of shapes. The theory has important applications in medical diagnostics, image analysis and machine vision.
Nonparametric Inference on Manifolds
Nonparametric Inference on Manifolds - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonparametric Inference on Manifolds write by Abhishek Bhattacharya. This book was released on 2014-05-14. Nonparametric Inference on Manifolds available in PDF, EPUB and Kindle. A systematic introduction to a general nonparametric theory of statistics on manifolds, with emphasis on manifolds of shapes.
Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis
Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis write by Victor Patrangenaru. This book was released on 2015-09-18. Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis available in PDF, EPUB and Kindle. A New Way of Analyzing Object Data from a Nonparametric ViewpointNonparametric Statistics on Manifolds and Their Applications to Object Data Analysis provides one of the first thorough treatments of the theory and methodology for analyzing data on manifolds. It also presents in-depth applications to practical problems arising in a variety of fields
Limit Theorems in Probability, Statistics and Number Theory
Limit Theorems in Probability, Statistics and Number Theory - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Limit Theorems in Probability, Statistics and Number Theory write by Peter Eichelsbacher. This book was released on 2013-04-23. Limit Theorems in Probability, Statistics and Number Theory available in PDF, EPUB and Kindle. Limit theorems and asymptotic results form a central topic in probability theory and mathematical statistics. New and non-classical limit theorems have been discovered for processes in random environments, especially in connection with random matrix theory and free probability. These questions and the techniques for answering them combine asymptotic enumerative combinatorics, particle systems and approximation theory, and are important for new approaches in geometric and metric number theory as well. Thus, the contributions in this book include a wide range of applications with surprising connections ranging from longest common subsequences for words, permutation groups, random matrices and free probability to entropy problems and metric number theory. The book is the product of a conference that took place in August 2011 in Bielefeld, Germany to celebrate the 60th birthday of Friedrich Götze, a noted expert in this field.
Methods and Theory for Nonparametric Inference In High-dimensional Settings
Methods and Theory for Nonparametric Inference In High-dimensional Settings - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Methods and Theory for Nonparametric Inference In High-dimensional Settings write by Yunhua Xiang. This book was released on 2021. Methods and Theory for Nonparametric Inference In High-dimensional Settings available in PDF, EPUB and Kindle. This dissertation addresses nonparametric estimation and inference problems of graphical modeling, linear association assessment, and matrix completion. First, we introduce a flexible framework for nonparametric graphical modeling. We propose three nonparametric measures of conditional dependence, which have theoretically optimal estimators that allow incorporation of flexible machine learning techniques and yield wald-type confidence intervals. In the second project, we propose a nonparametric parameter to measure the linear association between the outcome and explanatory variables. This parameter is always explicitly defined even when the true relationship is nonlinear and is equivalent with the regression coefficient under a linear model space. Thus, its estimator can be a more robust alternative to the standard model-based techniques to estimate the coefficients of a linear model. In the final project, we theoretically show that nuclear-norm penalization used for recovering low-rank matrices, remains effective even when the underlying matrices are generated by a low-dimensional non-linear manifold. The convergence rate can be expressed as a function of the size of the matrix, as well as the smoothness and dimension of the manifold, which is minimax optimal (up to a log term).
