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 characte...(展开全部)

The book covers new mathematical and computational principles for high-dimensional data analysis (statistics and geometry), scalable optimization methods (convex and nonconvex), and important applications such as scientific imaging, wideband communications, face recognition, 3D vision, and deep networks. This book is to be used as an introductory graduate textbook for the areas...(展开全部)

This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful.

<span>"This is a first-class book dealing with one of the most important areas of current research in applied statistics&#8230;the methods described are widely applicable&#8230;the standard of exposition is extremely high." <br />--Short Book Reviews from the International Statistical Institute</span></p> <span>"The new chapters (10-14) improve an already excellent resou...(展开全部)

Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnos...(展开全部)

Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describe...(展开全部)

This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for ...(展开全部)

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide—now including examples in Python as well as R—explains how to apply various statistical methods to data science, tells you how to avo...(展开全部)

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide—now including examples in Python as well as R—explains how to apply various statistical methods to data science, tells you how to avo...(展开全部)

Similiarity and Dimensional Methods in Mechanics, 10th Edition is an English language translation of this classic volume examining the general theory of dimensions of physical quantities, the theory of mechanical and physical similarity, and the theory of modeling. Several examples illustrate the use of the theories of similarity and dimensions for establishing fundamental mech...(展开全部)