Author by: Trevor Hastie Language: en Publisher by: Springer Science & Business Media Format Available: PDF, ePub, Mobi Total Read: 16 Total Download: 716 File Size: 41,8 Mb Description: During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology.
This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.
Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning.
Free Download Elements Of Statistical Computing Book Read online Elements Of Statistical Computing book that writen by R. Thisted in English language. Download and Read Elements Of Statistical Computing Elements Of Statistical Computing Change your habit to hang or waste the time to only chat with your friends.
The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title.
Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. Author by: R.A. Thisted Language: en Publisher by: Routledge Format Available: PDF, ePub, Mobi Total Read: 87 Total Download: 408 File Size: 54,5 Mb Description: Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science.
Elements of Statistical Computing covers the broad usage of computing in statistics. It provides a comprehensive account of the most important computational statistics.
Included are discussions of numerical analysis, numerical integration, and smoothing. The author give special attention to floating point standards and numerical analysis; iterative methods for both linear and nonlinear equation, such as Gauss-Seidel method and successive over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter, projection-pursuit methods, density estimation, and other computer-intensive techniques.
Author by: Boris Vladimirovich Gnedenko Language: en Publisher by: American Mathematical Soc. Format Available: PDF, ePub, Mobi Total Read: 55 Total Download: 746 File Size: 55,5 Mb Description: This classic book is intended to be the first introduction to probability and statistics written with an emphasis on the analytic approach to the problems discussed. Topics include the axiomatic setup of probability theory, polynomial distribution, finite Markov chains, distribution functions and convolution, the laws of large numbers (weak and strong), characteristic functions, the central limit theorem, infinitely divisible distributions, and Markov processes. Written in a clear and concise style, this book by Gnedenko can serve as a textbook for undergraduate and graduate courses in probability. Author by: James Bernard Ramsey Language: en Publisher by: South-Western Pub Format Available: PDF, ePub, Mobi Total Read: 98 Total Download: 945 File Size: 41,5 Mb Description: Designed for instructors who want to stress the understanding of basic concepts and the development of 'statistical intuition,' this book demonstrates that statistical reasoning is everywhere and that statistical concepts are as important to students' personal lives as they are to their future professional careers. Ramsey aims to develop statistically literacy - from the ability to read and think critically about statistics published in popular media to the ability to analyze and act upon statistics gathered in the business world.
The underlying philosophy of this book is that given a reasonable level of depth in the analysis, the student can later acquire a much more extensive, and even more intensive, exposure to statistics on their own or in the context of the work environment. Some use of calculus is included. Use of the computer is integrated throughout. Author by: D. Ter Haar Language: en Publisher by: Elsevier Format Available: PDF, ePub, Mobi Total Read: 14 Total Download: 336 File Size: 54,5 Mb Description: Following the Boltzmann-Gibbs approach to statistical mechanics, this new edition of Dr ter Haar's important textbook, Elements of Statistical Mechanics, provides undergraduates and more senior academics with a thorough introduction to the subject. Each chapter is followed by a problem section and detailed bibliography. The first six chapters of the book provide a thorough introduction to the basic methods of statistical mechanics and indeed the first four may be used as an introductory course in themselves.
The last three chapters offer more detail on the equation of state, with special emphasis on the van der Waals gas; the second-quantisation approach to many-body systems, with an examination of two-time temperature-dependent Green functions; phase transitions, including various approximation methods for treating the Ising model, a brief discussion of the exact solution of the two-dimensional square Ising model, and short introductions to renormalisation group methods and the Yang and Lee theory of phase transitions. In the problem section which follows each chapter the reader is asked to complete proofs of basic theory and to apply that theory to various physical situations. Each chapter bibliography includes papers which are of historical interest.
A further help to the reader are the solutions to selected problems which appear at the end of the book.
Author by: R.A. Thisted Language: en Publisher by: Routledge Format Available: PDF, ePub, Mobi Total Read: 53 Total Download: 904 File Size: 47,9 Mb Description: Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science. Elements of Statistical Computing covers the broad usage of computing in statistics. It provides a comprehensive account of the most important computational statistics.
Included are discussions of numerical analysis, numerical integration, and smoothing. The author give special attention to floating point standards and numerical analysis; iterative methods for both linear and nonlinear equation, such as Gauss-Seidel method and successive over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter, projection-pursuit methods, density estimation, and other computer-intensive techniques. Author by: Arkadiusz Sitek Language: en Publisher by: CRC Press Format Available: PDF, ePub, Mobi Total Read: 54 Total Download: 282 File Size: 42,8 Mb Description: Statistical Computing in Nuclear Imaging introduces aspects of Bayesian computing in nuclear imaging. The book provides an introduction to Bayesian statistics and concepts and is highly focused on the computational aspects of Bayesian data analysis of photon-limited data acquired in tomographic measurements. Basic statistical concepts, elements of decision theory, and counting statistics, including models of photon-limited data and Poisson approximations, are discussed in the first chapters. Monte Carlo methods and Markov chains in posterior analysis are discussed next along with an introduction to nuclear imaging and applications such as PET and SPECT.
The final chapter includes illustrative examples of statistical computing, based on Poisson-multinomial statistics. Examples include calculation of Bayes factors and risks as well as Bayesian decision making and hypothesis testing. Appendices cover probability distributions, elements of set theory, multinomial distribution of single-voxel imaging, and derivations of sampling distribution ratios. C++ code used in the final chapter is also provided. The text can be used as a textbook that provides an introduction to Bayesian statistics and advanced computing in medical imaging for physicists, mathematicians, engineers, and computer scientists.
It is also a valuable resource for a wide spectrum of practitioners of nuclear imaging data analysis, including seasoned scientists and researchers who have not been exposed to Bayesian paradigms. Author by: Roy C. Milton Language: en Publisher by: Academic Press Format Available: PDF, ePub, Mobi Total Read: 76 Total Download: 899 File Size: 40,5 Mb Description: Statistical Computation covers the proceedings of a conference held at the University of Wisconsin in Madison, Wisconsin on April 28-30, 1969.
The book focuses on the methodologies, techniques, principles, and approaches involved in statistical computation. The selection first elaborates on the description of data structures for statistical computing, autocodes for the statistician, and an experimental data structure for statistical computing. Discussions focus on data-system organization, data structures, autocode requirements, data matrix, structure formulas, and structure formulas in data processing and output. The text then examines statistics and computers in relation to large data bases, statistical data language, facilities in a statistical program system for analysis of multiply-indexed data, and language design and the needs of statisticians. Ibm Thinkcentre 8212 Sound Drivers more.
The book takes a look at time sharing and interactive statistics, an approach to conversational statistics, use of APL in statistics, and continuing development of a statistical system. Topics include arithmetic operations and branching statements, ASCOP system, application to statistics, semantics, pragmatics, and implementation. The selection is a valuable reference for statisticians and researchers interested in statistical computation. Author by: Aslak Tveito Language: en Publisher by: Springer Science & Business Media Format Available: PDF, ePub, Mobi Total Read: 89 Total Download: 104 File Size: 44,5 Mb Description: Science used to be experiments and theory, now it is experiments, theory and computations. The computational approach to understanding nature and technology is currently flowering in many fields such as physics, geophysics, astrophysics, chemistry, biology, and most engineering disciplines. This book is a gentle introduction to such computational methods where the techniques are explained through examples. It is our goal to teach principles and ideas that carry over from field to field.
You will learn basic methods and how to implement them. In order to gain the most from this text, you will need prior knowledge of calculus, basic linear algebra and elementary programming. Author by: Randall L. Eubank Language: en Publisher by: CRC Press Format Available: PDF, ePub, Mobi Total Read: 91 Total Download: 645 File Size: 42,5 Mb Description: With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response to this, Statistical Computing in C++ and R concentrates on the writing of code rather than the development and study of numerical algorithms per se. The book discusses code development in C++ and R and the use of these symbiotic languages in unison. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone.
The text begins with some basics of object-oriented languages, followed by a 'boot-camp' on the use of C++ and R. The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shell programming. Team Foundation Server Rapidshare Movies.
Features Includes numerous student exercises ranging from elementary to challenging Integrates both C++ and R for the solution of statistical computing problems Uses C++ code in R and R functions in C++ programs Provides downloadable programs, available from the authors’ website The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems. The text reveals the basic principles of algorithmic thinking essential to the modern statistician as well as the fundamental skill of communicating with a computer through the use of the computer languages C++ and R. The book lays the foundation for original code development in a research environment. Author by: Rick Wicklin Language: en Publisher by: SAS Institute Format Available: PDF, ePub, Mobi Total Read: 80 Total Download: 562 File Size: 44,8 Mb Description: SAS/IML software is a powerful tool for data analysts because it enables implementation of statistical algorithms that are not available in any SAS procedure. Rick Wicklin's Statistical Programming with SAS/IML Software is the first book to provide a comprehensive description of the software and how to use it. He presents tips and techniques that enable you to use the IML procedure and the SAS/IML Studio application efficiently. In addition to providing a comprehensive introduction to the software, the book also shows how to create and modify statistical graphs, call SAS procedures and R functions from a SAS/IML program, and implement such modern statistical techniques as simulations and bootstrap methods in the SAS/IML language.
Written for data analysts working in all industries, graduate students, and consultants, Statistical Programming with SAS/IML Software includes numerous code snippets and more than 100 graphs. This book is part of the SAS Press program.