Nancy Reid – författare

Likelihood serves as a unifying concept in both the theory and practice of statistical science. This is, in a sense, inevitable when probability models are used as a basis for inference. While the key ideas were set out in Fisher in 1922, and further developed throughout the 1930s and 40s, it was the ubiquity of the personal computer and the development of general-purpose software that made likelihood-based inference the method of choice in a wide variety of applications.

This book provides an overview of the many “adjective”-likelihood functions that have been developed in various contexts, aiming to include a wide array of inference functions used in the current literature, while recognizing that a comprehensive treatment is not possible, as research on likelihood-based inference continues.

This book is intended for readers with diverse backgrounds who have an interest in, or a need for, statistical methods in complex models. Some familiarity with likelihood-based inference and the main principles of estimation and hypothesis testing are assumed. The authors have used this text for senior undergraduate and graduate courses in inference.

The emergence of data science, in recent decades, has magnified the need for efficient methodology for analyzing data and highlighted the importance of statistical inference. Despite the tremendous progress that has been made, statistical science is still a young discipline and continues to have several different and competing paths in its approaches and its foundations. While the emergence of competing approaches is a natural progression of any scientific discipline, differences in the foundations of statistical inference can sometimes lead to different interpretations and conclusions from the same dataset. The increased interest in the foundations of statistical inference has led to many publications, and recent vibrant research activities in statistics, applied mathematics, philosophy and other fields of science reflect the importance of this development. The BFF approaches not only bridge foundations and scientific learning, but also facilitate objective and replicable scientific research, and provide scalable computing methodologies for the analysis of big data. Most of the published work typically focusses on a single topic or theme, and the body of work is scattered in different journals. This handbook provides a comprehensive introduction and broad overview of the key developments in the BFF schools of inference. It is intended for researchers and students who wish for an overview of foundations of inference from the BFF perspective and provides a general reference for BFF inference.

Key Features:

The emergence of data science, in recent decades, has magnified the need for efficient methodology for analyzing data and highlighted the importance of statistical inference. Despite the tremendous progress that has been made, statistical science is still a young discipline and continues to have several different and competing paths in its approaches and its foundations. While the emergence of competing approaches is a natural progression of any scientific discipline, differences in the foundations of statistical inference can sometimes lead to different interpretations and conclusions from the same dataset. The increased interest in the foundations of statistical inference has led to many publications, and recent vibrant research activities in statistics, applied mathematics, philosophy and other fields of science reflect the importance of this development. The BFF approaches not only bridge foundations and scientific learning, but also facilitate objective and replicable scientific research, and provide scalable computing methodologies for the analysis of big data. Most of the published work typically focusses on a single topic or theme, and the body of work is scattered in different journals. This handbook provides a comprehensive introduction and broad overview of the key developments in the BFF schools of inference. It is intended for researchers and students who wish for an overview of foundations of inference from the BFF perspective and provides a general reference for BFF inference.

Key Features:

Likelihood serves as a unifying concept in both the theory and practice of statistical science. This is, in a sense, inevitable when probability models are used as a basis for inference. While the key ideas were set out in Fisher in 1922, and further developed throughout the 1930s and 40s, it was the ubiquity of the personal computer and the development of general-purpose software that made likelihood-based inference the method of choice in a wide variety of applications.

This book provides an overview of the many “adjective”-likelihood functions that have been developed in various contexts, aiming to include a wide array of inference functions used in the current literature, while recognizing that a comprehensive treatment is not possible, as research on likelihood-based inference continues.

This book is intended for readers with diverse backgrounds who have an interest in, or a need for, statistical methods in complex models. Some familiarity with likelihood-based inference and the main principles of estimation and hypothesis testing are assumed. The authors have used this text for senior undergraduate and graduate courses in inference.