000			02859 a2200241 4500
003			OSt
005			20240425141339.0
008			181214b xxu||||| |||| 00| 0 eng d
020			_a9781118669303
040			_cIIT Kanpur
041			_aeng
082			_a519.5 _bC55r
100			_aClarke, Brenton R.
245			_aRobustness theory and application _cBrenton R. Clarke
260			_bWiley _cc2018 _aNew Jersey
300			_axxiii,215p
440			_aWiley series in probability and statistics
490			_a / edited by David J. Balding
500			_aincludes bibliographical references and index
520			_aA 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.
650			_aRobust statistics
942			_cBK
999			_c559847 _d559847