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.

Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention.

The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike.

Features:

Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention.

The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike.

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.

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.

Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention.

The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike.

Features:

Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention.

The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike.

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.