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All of Statistics (Springer Texts in Statistics)

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All of Statistics (Springer Texts in Statistics) Cover

ISBN13: 9780387402727
ISBN10: 0387402721
Condition:
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Synopses & Reviews

Publisher Comments:

This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level. Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal-Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics.

Synopsis:

WINNER OF THE 2005 DEGROOT PRIZE!

This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning.

This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level.

Synopsis:

WINNER OF THE 2005 DEGROOT PRIZE! This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level.

Table of Contents

Probability.- Random Variables.- Expectation.- Inequalities.- Convergence of Random Variables.- Models, Statistical Inference and Learning.- Estimating the CDF and Statistical Functionals.- The Bootstrap.- Parametric Inference.- Hypothesis Testing and p-values.- Bayesian Inference.- Statistical Decision Theory.- Linear and Logistic Regression.- Multivariate Models.- Inference about Independence.- Causal Inference.- Directed Graphs and Conditional Independence.- Undirected Graphs.- Loglinear Models.- Nonparametric Curve Estimation.- Smoothing Using Orthogonal Functions.- Classification.- Probability Redux: Stochastic Processes.- Simulation Methods.

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getdinformation, February 24, 2007 (view all comments by getdinformation)
AM GLAD TO COME ACROSS ALL OF STATISTICS:
BECAUSE, ITS TITLED SIMPLY CREAT AN IMPRESSION OF ASSURANCE OF ALL WHAT IS NEEDED ABOUT STATISTICS...INFACT,IT IT'S INDEED A CONCISE COURSE IN STATISTICAL INFERENCE...THANKS FOR THIS PRRIVELEDGE.
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Product Details

ISBN:
9780387402727
Author:
Wasserman, Larry A.
Publisher:
Springer
Author:
Wasserman, Larry
Location:
New York, NY
Subject:
Computer Science
Subject:
Statistics
Subject:
Mathematical statistics
Subject:
Probability & Statistics - General
Subject:
Mathematical & Statistical Software
Subject:
Statistical Theory and Methods
Subject:
Probability and Statistics in Computer Science
Subject:
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences This book surveys a broad range of topics in probability and mathematical statistics. It provides the statistical background that a computer scientist needs to work in the
Subject:
Mathematics | Probability and Statistics
Subject:
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Subject:
Probability
Subject:
and Statistics in Computer Science
Subject:
B
Subject:
mathematics and statistics
Copyright:
Edition Number:
1
Edition Description:
2004. Corr. 2nd
Series:
Springer Texts in Statistics
Series Volume:
6
Publication Date:
September 2004
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
461
Dimensions:
235 x 155 mm 1830 gr

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All of Statistics (Springer Texts in Statistics) New Hardcover
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$108.50 In Stock
Product details 461 pages Springer Us - English 9780387402727 Reviews:
"Synopsis" by , WINNER OF THE 2005 DEGROOT PRIZE!

This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning.

This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level.

"Synopsis" by , WINNER OF THE 2005 DEGROOT PRIZE! This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level.
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