Mathematical Foundations Of Infinite Dimensional Statistical Models PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Mathematical Foundations Of Infinite Dimensional Statistical Models PDF full book. Access full book title Mathematical Foundations Of Infinite Dimensional Statistical Models.
| Author | : Evarist Giné |
| Publisher | : Cambridge University Press |
| Total Pages | : 706 |
| Release | : 2021-03-25 |
| Genre | : Mathematics |
| ISBN | : 1009022784 |
Download Mathematical Foundations of Infinite-Dimensional Statistical Models Book in PDF, ePub and Kindle
In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics.
| Author | : David Williams |
| Publisher | : Cambridge University Press |
| Total Pages | : 274 |
| Release | : 1991-02-14 |
| Genre | : Mathematics |
| ISBN | : 9780521406055 |
Download Probability with Martingales Book in PDF, ePub and Kindle
This is a masterly introduction to the modern, and rigorous, theory of probability. The author emphasises martingales and develops all the necessary measure theory.
| Author | : Jianqing Fan |
| Publisher | : CRC Press |
| Total Pages | : 942 |
| Release | : 2020-09-21 |
| Genre | : Mathematics |
| ISBN | : 0429527616 |
Download Statistical Foundations of Data Science Book in PDF, ePub and Kindle
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
| Author | : Subhashis Ghosal |
| Publisher | : Cambridge University Press |
| Total Pages | : 671 |
| Release | : 2017-06-26 |
| Genre | : Business & Economics |
| ISBN | : 0521878268 |
Download Fundamentals of Nonparametric Bayesian Inference Book in PDF, ePub and Kindle
Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.
| Author | : Aleksandr Petrovich Korostelev |
| Publisher | : American Mathematical Soc. |
| Total Pages | : 258 |
| Release | : 2011 |
| Genre | : Mathematics |
| ISBN | : 0821852833 |
Download Mathematical Statistics Book in PDF, ePub and Kindle
iPositive Give a man a fish, he eats for a day, but if you teach him to fish, you feed him for life. Such is the approach of iPositive. One day at the gym doesnt make a person fit for life; its a consistent dedication to getting the body in shape that eventually yields results. The lessons in iPositive work in much the same way: They challenge the reader to work to keep the mind in shape. The book is a powerful guide to personal happiness through positivity. Its concepts provide empowerment to overcome self-doubt, disbelief and inferiority complexes in order to transcend the negativity in life. iPositive is geared toward helping individuals become more focused on the things they most want in life, like happiness, love and success, or banish anchors that may be weighting them down, like stress, smoking or excess weight. The book gives readers the practical means to become more focused on those things they want in life, and serves as an inspirational manual for a life of fulfillment, and strength in body, mind and spirit.
| Author | : Olav Kallenberg |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 536 |
| Release | : 2005-07-27 |
| Genre | : Mathematics |
| ISBN | : 9780387251158 |
Download Probabilistic Symmetries and Invariance Principles Book in PDF, ePub and Kindle
This is the first comprehensive treatment of the three basic symmetries of probability theory—contractability, exchangeability, and rotatability—defined as invariance in distribution under contractions, permutations, and rotations. Originating with the pioneering work of de Finetti from the 1930's, the theory has evolved into a unique body of deep, beautiful, and often surprising results, comprising the basic representations and invariance properties in one and several dimensions, and exhibiting some unexpected links between the various symmetries as well as to many other areas of modern probability. Most chapters require only some basic, graduate level probability theory, and should be accessible to any serious researchers and graduate students in probability and statistics. Parts of the book may also be of interest to pure and applied mathematicians in other areas. The exposition is formally self-contained, with detailed references provided for any deeper facts from real analysis or probability used in the book. Olav Kallenberg received his Ph.D. in 1972 from Chalmers University in Gothenburg, Sweden. After teaching for many years at Swedish universities, he moved in 1985 to the US, where he is currently Professor of Mathematics at Auburn University. He is well known for his previous books Random Measures (4th edition, 1986) and Foundations of Modern Probability (2nd edition, 2002) and for numerous research papers in all areas of probability. In 1977, he was the second recipient ever of the prestigious Rollo Davidson Prize from Cambridge University. In 1991–94, he served as the Editor in Chief of Probability Theory and Related Fields. Professor Kallenberg is an elected fellow of the Institute of Mathematical Statistics.
| Author | : Bert E. Fristedt |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 775 |
| Release | : 2013-11-21 |
| Genre | : Mathematics |
| ISBN | : 1489928375 |
Download A Modern Approach to Probability Theory Book in PDF, ePub and Kindle
Students and teachers of mathematics and related fields will find this book a comprehensive and modern approach to probability theory, providing the background and techniques to go from the beginning graduate level to the point of specialization in research areas of current interest. The book is designed for a two- or three-semester course, assuming only courses in undergraduate real analysis or rigorous advanced calculus, and some elementary linear algebra. A variety of applications—Bayesian statistics, financial mathematics, information theory, tomography, and signal processing—appear as threads to both enhance the understanding of the relevant mathematics and motivate students whose main interests are outside of pure areas.
| Author | : Roman Vershynin |
| Publisher | : Cambridge University Press |
| Total Pages | : 299 |
| Release | : 2018-09-27 |
| Genre | : Business & Economics |
| ISBN | : 1108415199 |
Download High-Dimensional Probability Book in PDF, ePub and Kindle
An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
| Author | : Herman J. Bierens |
| Publisher | : Cambridge University Press |
| Total Pages | : 356 |
| Release | : 2004-12-20 |
| Genre | : Business & Economics |
| ISBN | : 9780521542241 |
Download Introduction to the Mathematical and Statistical Foundations of Econometrics Book in PDF, ePub and Kindle
This book is intended for use in a rigorous introductory PhD level course in econometrics.
| Author | : Larry Wasserman |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 446 |
| Release | : 2013-12-11 |
| Genre | : Mathematics |
| ISBN | : 0387217363 |
Download All of Statistics Book in PDF, ePub and Kindle
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
