Probability and Conditional Expectation: Fundamentals for the Empirical Sciences (Wiley Series in Probability and Statistics) - Hardcover

Steyer, Rolf; Nagel, Werner

Hardcover

ISBN 10: 1119243521 ISBN 13: 9781119243526

Publisher: Wiley, 2017

Synopsis

Probability and Conditional Expectations bridges the gap between books on probability theory and statistics by providing the probabilistic concepts estimated and tested in analysis of variance, regression analysis, factor analysis, structural equation modeling, hierarchical linear models and analysis of qualitative data. The authors emphasize the theory of conditional expectations that is also fundamental to conditional independence and conditional distributions.

Probability and Conditional Expectations

• Presents a rigorous and detailed mathematical treatment of probability theory focusing on concepts that are fundamental to understand what we are estimating in applied statistics.
• Explores the basics of random variables along with extensive coverage of measurable functions and integration.
• Extensively treats conditional expectations also with respect to a conditional probability measure and the concept of conditional effect functions, which are crucial in the analysis of causal effects.
• Is illustrated throughout with simple examples, numerous exercises and detailed solutions.
• Provides website links to further resources including videos of courses delivered by the authors as well as R code exercises to help illustrate the theory presented throughout the book.

About this title

About the Author

Rolf Steyer,
Institute of Psychology, University of Jena, Germany

Werner Nagel,
Institute of Mathematics, University of Jena, Germany

From the Back Cover

This book bridges the gap between books on probability theory and statistics by providing the probabilistic concepts estimated and tested in the analysis of variance, regression analysis, factor analysis, structural equation modeling, hierarchical linear models, and analysis of qualitative data. The authors emphasize the theory of conditional expectations that is also fundamental to conditional independence and conditional distributions.

Key features:

• Presents a rigorous and detailed mathematical treatment of probability theory, focusing on concepts that are fundamental to understand what we are estimating in applied statistics
• Explores the basics of random variables along with extensive coverage of measurable functions and integration.
• Extensively treats conditional expectations with respect to a conditional probability measure and the concept of conditional effect functions, which are crucial in the analysis of causal effects.
• Is illustrated throughout with simple examples, numerous exercises, and detailed solutions.
• Provides website links to further resources, including videos of courses delivered by the authors as well as R code exercises to help illustrate the theory presented throughout the book.

Aimed at mathematicians, applied statisticians and substantive researchers, this book will help readers to understand in terms of probability theory what applied statisticians and substantive researchers estimate and test in their empirical studies.

From the Inside Flap

This book bridges the gap between books on probability theory and statistics by providing the probabilistic concepts estimated and tested in the analysis of variance, regression analysis, factor analysis, structural equation modeling, hierarchical linear models, and analysis of qualitative data. The authors emphasize the theory of conditional expectations that is also fundamental to conditional independence and conditional distributions.

Key features:

• Presents a rigorous and detailed mathematical treatment of probability theory, focusing on concepts that are fundamental to understand what we are estimating in applied statistics
• Explores the basics of random variables along with extensive coverage of measurable functions and integration.
• Extensively treats conditional expectations with respect to a conditional probability measure and the concept of conditional effect functions, which are crucial in the analysis of causal effects.
• Is illustrated throughout with simple examples, numerous exercises, and detailed solutions.
• Provides website links to further resources, including videos of courses delivered by the authors as well as R code exercises to help illustrate the theory presented throughout the book.

Aimed at mathematicians, applied statisticians and substantive researchers, this book will help readers to understand in terms of probability theory what applied statisticians and substantive researchers estimate and test in their empirical studies.

Edition details

• Publisher: Wiley
• Publication date: 2017
• Language: English
• ISBN 10: 1119243521
• ISBN 13: 9781119243526
• Binding: Hardcover
• Edition number: 1
• Number of pages: 600