Essentials of Statistical Inference

Author: G. A. Young,R. L. Smith,R. L. (University of North Carolina Smith, Chapel Hill)

Publisher: Cambridge University Press

ISBN: 9780521839716

Category: Mathematics

Page: 225

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Concise account of main approaches; first textbook to synthesize modern computation with basic theory.
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Events in Social Networks

A Stochastic Actor-oriented Framework for Dynamic Event Processes in Social Networks

Author: Christoph Stadtfeld

Publisher: KIT Scientific Publishing

ISBN: 3866447922

Category: Social networks

Page: 140

View: 2514

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Brownian Motion

Author: Peter Mörters,Yuval Peres

Publisher: Cambridge University Press

ISBN: 1139486578

Category: Mathematics

Page: N.A

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This eagerly awaited textbook covers everything the graduate student in probability wants to know about Brownian motion, as well as the latest research in the area. Starting with the construction of Brownian motion, the book then proceeds to sample path properties like continuity and nowhere differentiability. Notions of fractal dimension are introduced early and are used throughout the book to describe fine properties of Brownian paths. The relation of Brownian motion and random walk is explored from several viewpoints, including a development of the theory of Brownian local times from random walk embeddings. Stochastic integration is introduced as a tool and an accessible treatment of the potential theory of Brownian motion clears the path for an extensive treatment of intersections of Brownian paths. An investigation of exceptional points on the Brownian path and an appendix on SLE processes, by Oded Schramm and Wendelin Werner, lead directly to recent research themes.
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Frontiers in Statistical Quality Control 11

Author: Sven Knoth,Wolfgang Schmid

Publisher: Springer

ISBN: 3319123556

Category: Computers

Page: 393

View: 3629

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The main focus of this edited volume is on three major areas of statistical quality control: statistical process control (SPC), acceptance sampling and design of experiments. The majority of the papers deal with statistical process control, while acceptance sampling and design of experiments are also treated to a lesser extent. The book is organized into four thematic parts, with Part I addressing statistical process control. Part II is devoted to acceptance sampling. Part III covers the design of experiments, while Part IV discusses related fields. The twenty-three papers in this volume stem from The 11th International Workshop on Intelligent Statistical Quality Control, which was held in Sydney, Australia from August 20 to August 23, 2013. The event was hosted by Professor Ross Sparks, CSIRO Mathematics, Informatics and Statistics, North Ryde, Australia and was jointly organized by Professors S. Knoth, W. Schmid and Ross Sparks. The papers presented here were carefully selected and reviewed by the scientific program committee, before being revised and adapted for this volume.
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Understanding Probability

Author: Henk Tijms

Publisher: Cambridge University Press

ISBN: 110765856X

Category: Mathematics

Page: 562

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Using everyday examples to demystify probability, this classic is now in its third edition with new chapters, exercises and examples.
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Causal Inference for Statistics, Social, and Biomedical Sciences

An Introduction

Author: Guido W. Imbens,Donald B. Rubin

Publisher: Cambridge University Press

ISBN: 1316094391

Category: Mathematics

Page: N.A

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Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.
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