By Peter J. Rousseeuw
Presents an applications-oriented creation to strong regression and outlier detection, emphasising °high-breakdown° tools that could take care of a titanic fraction of infection. Its self-contained therapy permits readers to bypass the mathematical fabric that's focused in a number of sections. Exposition makes a speciality of the least median of squares process, that is intuitive and straightforward to exploit, and plenty of real-data examples are given. bankruptcy insurance contains strong a number of regression, the designated case of one-dimensional place, algorithms, outlier diagnostics, and robustness in similar fields, comparable to the estimation of multivariate position and covariance matrices, and time sequence research.
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Additional resources for Robust regression and outlier detection
Sample text
The FORTRAN format has to be set between brackets, and it should be described in at most 60 characters (including the brackets). The observations are to be processed as real numbers, so they should be read in F-formats and/or E-formats. The formats X and / are also allowed. Because the execution time for large data sets may be quite long, the user has the option of choosing a faster version of the algorithm in that case. In other cases it is recommended to use the extensive search version because of its greater precision.
However, we shall postpone the discussion of these options until Section 2 of Chapter 3. COMPUTATION OF THE LEAST MEDIAN OF SQUARES LINE 35 CHOOSE AN OPTION FOR THE TREATMENT OF MISSING VALUES ....................................... O=THERE ARE NO MISSING VALUES IN THE DATA 1-ELIMINATION OF THE CASES FOR WHICH AT LEAST ONE VARIABLE IS MISSING 2=ESTIMATES ARE FILLED IN FOR UNOBSERVED VALUES ENTER YOUR CHOICE: 0 Finally, PROGRESS gives a survey of the options that were selected. * P R O G R E S S WILL PERFORM A REGRESSION WITH CONSTANT TERM * ..................................................
Chapter 3 also contains the theoretical robustness results. In Chapter 4 the special case of estimation of one-dimensional location is covered. Chapter 5 discusses several algorithms for high-breakdown regression, and also reports on a small simulation study. Chapter 6 gives a survey of outlier diagnostics, including recent developments. Chapter 7 treats the extension of the LMS,LTS,and S-estimators to related areas such as multivariate location and covariance estimation, time series, and orthogonal regression.
