Description
Condition: BRAND NEW
ISBN: 9781118669303
Year: 2018
Publisher: John Wiley & Sons Inc (US)
Pages: 240
Description:
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 l
ISBN: 9781118669303
Year: 2018
Publisher: John Wiley & Sons Inc (US)
Pages: 240
Description:
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 l