All Of Statistics Larry Wasserman Pdf


All Of Statistics Larry Wasserman Pdf
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All Of Statistics


All Of Statistics
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Author : Larry Wasserman
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-12-11

All Of Statistics written by Larry Wasserman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-11 with Mathematics categories.


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.



All Of Nonparametric Statistics


All Of Nonparametric Statistics
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Author : Larry Wasserman
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-10

All Of Nonparametric Statistics written by Larry Wasserman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-10 with Mathematics categories.


This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.



A Concise Course In Advanced Level Statistics


A Concise Course In Advanced Level Statistics
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Author : Janet Crawshaw
language : en
Publisher: Nelson Thornes
Release Date : 2001

A Concise Course In Advanced Level Statistics written by Janet Crawshaw and has been published by Nelson Thornes this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Mathematics categories.


New in this edition is a 20 page section on the use of ICT resources in teaching and learning about statistics. The book also includes over 300 worked examples and advice on how to break down calculations into easy stages.



Statistical Inference As Severe Testing


Statistical Inference As Severe Testing
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Author : Deborah G. Mayo
language : en
Publisher: Cambridge University Press
Release Date : 2018-09-20

Statistical Inference As Severe Testing written by Deborah G. Mayo and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-20 with Mathematics categories.


Unlock today's statistical controversies and irreproducible results by viewing statistics as probing and controlling errors.



Mathematics For Machine Learning


Mathematics For Machine Learning
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Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23

Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-23 with Computers categories.


Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.



Statistics In A Nutshell


Statistics In A Nutshell
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Author : Sarah Boslaugh
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2012-11-15

Statistics In A Nutshell written by Sarah Boslaugh and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-15 with Mathematics categories.


A clear and concise introduction and reference for anyone new to the subject of statistics.



High Dimensional Statistics


High Dimensional Statistics
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Author : Martin J. Wainwright
language : en
Publisher: Cambridge University Press
Release Date : 2019-02-21

High Dimensional Statistics written by Martin J. Wainwright and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with Business & Economics categories.


A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.



An Introduction To Statistical Learning


An Introduction To Statistical Learning
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Author : Gareth James
language : en
Publisher: Springer Nature
Release Date : 2023-08-01

An Introduction To Statistical Learning written by Gareth James and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-01 with Mathematics categories.


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, marketing, and 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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.



A Modern Introduction To Probability And Statistics


A Modern Introduction To Probability And Statistics
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Author : F.M. Dekking
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-03-30

A Modern Introduction To Probability And Statistics written by F.M. Dekking and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-03-30 with Mathematics categories.


Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books



Theoretical Statistics


Theoretical Statistics
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Author : Robert W. Keener
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09-08

Theoretical Statistics written by Robert W. Keener and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-08 with Mathematics categories.


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.