𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Outlier Detection Using Nonconvex Penalized Regression

✍ Scribed by She, Yiyuan; Owen, Art B.

Book ID
120832041
Publisher
American Statistical Association
Year
2011
Tongue
English
Weight
655 KB
Volume
106
Category
Article
ISSN
0162-1459

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

Outlier detection by robust alternating
Outlier detection by robust alternating regression
✍ Γ…smund Ukkelberg; Odd S. Borgen πŸ“‚ Article πŸ“… 1993 πŸ› Elsevier Science 🌐 English βš– 528 KB

The sum of least-squares regression method is normally used when principal components are extracted from a data matrix. This may result in a misleading set of principal components if outliers are present in the data set, in terms of both the number of components and their direction in vector space.

Outlier Detection in Regression Models w
Outlier Detection in Regression Models with ARIMA Errors using Robust Estimates
✍ A. M. Bianco; M. GarcΓ­a Ben; E. J. MartΓ­nez; V. J. Yohai πŸ“‚ Article πŸ“… 2001 πŸ› John Wiley and Sons 🌐 English βš– 130 KB

## Abstract A diagnostic procedure for detecting additive and innovation outliers as well as level shifts in a regression model with ARIMA errors is introduced. The procedure is based on a robust estimate of the model parameters and on innovation residuals computed by means of robust filtering. A M

Note on the use of stepwise regression i
Note on the use of stepwise regression in detecting outliers
✍ Max R. Mickey; Olive Jean Dunn; Virginia Clark πŸ“‚ Article πŸ“… 1967 πŸ› Elsevier Science 🌐 English βš– 333 KB

Cases can be considered as possible outliers in a regression structure if deletion results in a sufficiently large reduction of the residual sum of squares. Calculations for selection of outlier cases on this basis can be accomplished by use of stepwise regression programs. Numerical examples are in