Download Free Introduction To High Dimensional Statistics Book in PDF and EPUB Free Download. You can read online Introduction To High Dimensional Statistics and write the review.

Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians
Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.
"An Introduction to Regularized Estimation in High Dimensions considers statistical theory, methods, and algorithms for large and complex data. The main focus is on regularized estimators, which are at the cusp of entering the statistical toolkits of almost all scientific disciplines. This book provides clear expositions, motivational introductions to each chapter, rigorous step-by-step proofs, and comprehensive exercise sets with fully worked out solutions. These features make this book ideal for graduate level courses. Moreover, the book also discusses cutting-edge topics, such as aspects of inference, robustness, and tuning parameters. The book also contains results and insights that are new altogether, including improvements of existing theories and novel connections among different methods, which are organized into special chapters for those wishing to advance their reading in the field."--
An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.
Written by leading experts in the field, this book presents the most recent developments in the use of random matrix theory in high-dimensional statistics.
This volume is composed of peer-reviewed papers that have developed from the First Conference of the International Society for Non Parametric Statistics (ISNPS). This inaugural conference took place in Chalkidiki, Greece, June 15-19, 2012. It was organized with the co-sponsorship of the IMS, the ISI and other organizations. M.G. Akritas, S.N. Lahiri and D.N. Politis are the first executive committee members of ISNPS and the editors of this volume. ISNPS has a distinguished Advisory Committee that includes Professors R.Beran, P.Bickel, R. Carroll, D. Cook, P. Hall, R. Johnson, B. Lindsay, E. Parzen, P. Robinson, M. Rosenblatt, G. Roussas, T. SubbaRao and G. Wahba. The Charting Committee of ISNPS consists of more than 50 prominent researchers from all over the world. The chapters in this volume bring forth recent advances and trends in several areas of nonparametric statistics. In this way, the volume facilitates the exchange of research ideas, promotes collaboration among researchers from all over the world and contributes to the further development of the field. The conference program included over 250 talks, including special invited talks, plenary talks and contributed talks on all areas of nonparametric statistics. Out of these talks, some of the most pertinent ones have been refereed and developed into chapters that share both research and developments in the field.
This text presents an engaging exposition of the active field of high-dimensional percolation that will likely provide an impetus for future work. With over 90 exercises designed to enhance the reader’s understanding of the material, as well as many open problems, the book is aimed at graduate students and researchers who wish to enter the world of this rich topic. The text may also be useful in advanced courses and seminars, as well as for reference and individual study. Part I, consisting of 3 chapters, presents a general introduction to percolation, stating the main results, defining the central objects, and proving its main properties. No prior knowledge of percolation is assumed. Part II, consisting of Chapters 4–9, discusses mean-field critical behavior by describing the two main techniques used, namely, differential inequalities and the lace expansion. In Parts I and II, all results are proved, making this the first self-contained text discussing high-dime nsional percolation. Part III, consisting of Chapters 10–13, describes recent progress in high-dimensional percolation. Partial proofs and substantial overviews of how the proofs are obtained are given. In many of these results, the lace expansion and differential inequalities or their discrete analogues are central. Part IV, consisting of Chapters 14–16, features related models and further open problems, with a focus on the big picture.
Recent advances in high throughput biological methods allow researchers to generate enormous amounts of data from a single experiment. In order to extract meaningful conclusions from this tidal wave of data, it will be necessary to develop analytical methods of sufficient power and utility. It is particularly important that biologists themselves perform many of these analyses, such that their background knowledge of the experimental system under study can be used to interpret results and direct further inquiries.
The interest towards Functional and Operatorial Statistics, and, more in general, towards infinite-dimensional statistics has dramatically increased in the statistical community and in many other applied scientific areas where people faces functional data. This volume collects the works selected and presented at the Third Edition of the International Workshop on Functional and Operatorial Statistics held in Stresa, Italy, from the 19th to the 21st of June 2014 (IWFOS’2014). The meeting represents an opportunity of bringing together leading researchers active on these topics both for what concerns theoretical aspects and a wide range of applications in various fields. To promote collaborations with other important strictly related areas of infinite-dimensional Statistics, such as High Dimensional Statistics and Model Selection Procedures, this book hosts works in the latter research subjects too.

Best Books