A preeminent expert in the field explores new and exciting methodologies in the ever-growing field of robust statistics Used to develop data analytical methods, which are resistant to outlying observations in the data, while capable of detecting outliers, robust statistics is extremely useful for solving an array of common problems, such as estimating location, scale, and regression parameters. Written by an internationally recognized expert in the field of robust statistics, this book addresses a range of well-established techniques while exploring, in depth, new and exciting methodologies. Local robustness and global robustness are discussed, and problems of non-identifiability and adaptive estimation are considered. Rather than attempt an exhaustive investigation of robustness, the author provides readers with a timely review of many of the most important problems in statistical inference involving robust estimation, along with a brief look at confidence intervals for location. Throughout, the author meticulously links research in maximum likelihood estimation with the more general M-estimation methodology. Specific applications and R and some MATLAB subroutines with accompanying data sets-available both in the text and online-are employed wherever appropriate. Providing invaluable insights and guidance, Robustness Theory and Application:� Offers a balanced presentation of theory and applications within each topic-specific discussionFeatures solved examples throughout which help clarify complex and/or difficult conceptsMeticulously links research in maximum likelihood type estimation with the more general M-estimation methodologyDelves into new methodologies which have been developed over the past decade without stinting on coverage of "tried-and-true" methodologiesIncludes R and some MATLAB subroutines with accompanying data sets, which help illustrate the power of the methods described Robustness Theory and Application is an important resource for all statisticians interested in the topic of robust statistics. This book encompasses both past and present research, making it a valuable supplemental text for graduate-level courses in robustness.

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Detalles bibliogr�ficos

T�tulo: Robustness Theory and Application
Editor: John Wiley and Sons Inc, US
Fecha de publicaci�n: 2018
Idioma: Ingl�s
ISBN 10: 1118669304
ISBN 13: 9781118669303
Encuadernaci�n: Hardback
Estado: New
Peso: 536 gramos

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Sinopsis

A preeminent expert in the field explores new and exciting methodologies in the ever-growing field of robust statistics

Used to develop data analytical methods, which are resistant to outlying observations in the data, while capable of detecting outliers, robust statistics is extremely useful for solving an array of common problems, such as estimating location, scale, and regression parameters. Written by an internationally recognized expert in the field of robust statistics, this book addresses a range of well-established techniques while exploring, in depth, new and exciting methodologies. Local robustness and global robustness are discussed, and problems of non-identifiability and adaptive estimation are considered. Rather than attempt an exhaustive investigation of robustness, the author provides readers with a timely review of many of the most important problems in statistical inference involving robust estimation, along with a brief look at confidence intervals for location. Throughout, the author meticulously links research in maximum likelihood estimation with the more general M-estimation methodology. Specific applications and R and some MATLAB subroutines with accompanying data sets―available both in the text and online―are employed wherever appropriate.

Providing invaluable insights and guidance, Robustness Theory and Application:

Offers a balanced presentation of theory and applications within each topic-specific discussion
Features solved examples throughout which help clarify complex and/or difficult concepts
Meticulously links research in maximum likelihood type estimation with the more general M-estimation methodology
Delves into new methodologies which have been developed over the past decade without stinting on coverage of “tried-and-true” methodologies
Includes R and some MATLAB subroutines with accompanying data sets, which help illustrate the power of the methods described

Robustness Theory and Application is an important resource for all statisticians interested in the topic of robust statistics. This book encompasses both past and present research, making it a valuable supplemental text for graduate-level courses in robustness.

Acerca del autor

Brenton R. Clarke, PhD is an experienced academic in Mathematics and Statistics at Murdoch University, Perth, WA, Australia. A former president of the Western Australian Branch of the Statistical Society of Australia, Dr. Clarke has published numerous journal articles in his areas of research interest, which include linear models, robust statistics, and time series analysis.

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