Download stat labs mathematical statistics through applications springer texts in statistics in pdf or read stat labs mathematical statistics through applications springer texts in statistics in pdf online books in PDF, EPUB and Mobi Format. Click Download or Read Online button to get stat labs mathematical statistics through applications springer texts in statistics in pdf book now. This site is like a library, Use search box in the widget to get ebook that you want.



Stat Labs

Author: Deborah Nolan
Publisher: Springer Science & Business Media
ISBN: 0387227431
Size: 34.42 MB
Format: PDF, Docs
View: 7673
Download and Read
Integrating the theory and practice of statistics through a series of case studies, each lab introduces a problem, provides some scientific background, suggests investigations for the data, and provides a summary of the theory used in each case. Aimed at upper-division students.

Stat Labs

Author: Deborah Nolan
Publisher: Springer Science & Business Media
ISBN: 0387989749
Size: 52.69 MB
Format: PDF, Mobi
View: 3183
Download and Read
This well-rounded introduction to modern statistical practice combines background theory and a series of case studies. Each lab in the book introduces a real problem, provides background material on the problem, describes the data that can solve it, and summarizes the theory used in the investigations. 45 illus.

Mathematical Statistics

Author: George R. Terrell
Publisher: Springer Science & Business Media
ISBN: 0387227695
Size: 64.29 MB
Format: PDF, ePub
View: 1498
Download and Read
This textbook introduces the mathematical concepts and methods that underlie statistics. The course is unified, in the sense that no prior knowledge of probability theory is assumed, being developed as needed. The book is committed to both a high level of mathematical seriousness and to an intimate connection with application. In its teaching style, the book is * mathematically complete * concrete * constructive * active. The text is aimed at the upper undergraduate or the beginning Masters program level. It assumes the usual two-year college mathematics sequence, including an introduction to multiple integrals, matrix algebra, and infinite series.

An Introduction To Statistical Learning

Author: Gareth James
Publisher: Springer Science & Business Media
ISBN: 1461471389
Size: 70.81 MB
Format: PDF, Docs
View: 800
Download and Read
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

All Of Statistics

Author: Larry Wasserman
Publisher: Springer Science & Business Media
ISBN: 0387217363
Size: 40.82 MB
Format: PDF, Docs
View: 548
Download and Read
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.

Asymptotic Theory Of Statistics And Probability

Author: Anirban DasGupta
Publisher: Springer Science & Business Media
ISBN: 0387759700
Size: 54.13 MB
Format: PDF, Mobi
View: 5901
Download and Read
This unique book delivers an encyclopedic treatment of classic as well as contemporary large sample theory, dealing with both statistical problems and probabilistic issues and tools. The book is unique in its detailed coverage of fundamental topics. It is written in an extremely lucid style, with an emphasis on the conceptual discussion of the importance of a problem and the impact and relevance of the theorems. There is no other book in large sample theory that matches this book in coverage, exercises and examples, bibliography, and lucid conceptual discussion of issues and theorems.

Statistics And Finance

Author: David Ruppert
Publisher: Springer
ISBN: 1441968768
Size: 41.85 MB
Format: PDF, ePub, Docs
View: 721
Download and Read
This book emphasizes the applications of statistics and probability to finance. The basics of these subjects are reviewed and more advanced topics in statistics, such as regression, ARMA and GARCH models, the bootstrap, and nonparametric regression using splines, are introduced as needed. The book covers the classical methods of finance and it introduces the newer area of behavioral finance. Applications and use of MATLAB and SAS software are stressed. The book will serve as a text in courses aimed at advanced undergraduates and masters students. Those in the finance industry can use it for self-study.

Theoretical Statistics

Author: Robert W. Keener
Publisher: Springer Science & Business Media
ISBN: 9780387938394
Size: 61.57 MB
Format: PDF
View: 186
Download and Read
Intended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential. The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis. The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix.

Mathematical Methods Of Statistics Pms 9

Author: Harald Cramér
Publisher: Princeton University Press
ISBN: 1400883865
Size: 60.26 MB
Format: PDF, Docs
View: 3148
Download and Read
In this classic of statistical mathematical theory, Harald Cramér joins the two major lines of development in the field: while British and American statisticians were developing the science of statistical inference, French and Russian probabilitists transformed the classical calculus of probability into a rigorous and pure mathematical theory. The result of Cramér's work is a masterly exposition of the mathematical methods of modern statistics that set the standard that others have since sought to follow. For anyone with a working knowledge of undergraduate mathematics the book is self contained. The first part is an introduction to the fundamental concept of a distribution and of integration with respect to a distribution. The second part contains the general theory of random variables and probability distributions while the third is devoted to the theory of sampling, statistical estimation, and tests of significance.