A comparative analysis of multiple outlier detection procedures in the linear regression model

Computational Statistics and Data Analysis

Volumn 36, Issue 3, 2001, Pages 351-382

  Wisnowski, James W ^a   Montgomery, Douglas C ^b   Simpson, James R ^c  

^a UNITED STATES AIR FORCE ACADEMY   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

^c FLORIDA STATE UNIVERSITY   (United States)

Author keywords

Minimum volume ellipsoid; Monte Carlo simulation; Multiple outliers; Outlier; Robust regression

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; MONTE CARLO METHODS; PROBABILITY;

LINEAR REGRESSION;

REGRESSION ANALYSIS;

ALGORITHM; COMPARATIVE STUDY; MONTE CARLO ANALYSIS;

EID: 0035962712     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-9473(00)00042-6     Document Type: Article

References (20)

1. Bivariate boxplots, multiple outliers, multivariate transformations and discriminant analysis: The 1997 hunter lecture
2. A bounded influence, high breakdown, efficient regression estimator
3. Identifying multiple outliers in multivariate data
4. A modification of a method for the detection of outliers in multivariate samples
5. Procedures for the identification of multiple outliers in linear models
6. A more robust outlier identifier for regression data
7. Robust regression: Asymptotics, conjectures and Monte Carlo
8. A Monte Carlo comparison of five procedures for identifying outliers in linear regression
9. Algorithms, routines, and S functions for robust statistics
10. Identification of outliers in multivariate data
11. Multivariate estimation with high breakdown point
12. Least median of squares regression
13. Unmasking multivariate outliers and leverage points
14. A clustering algorithm for identifying multiple outliers in linear regression
15. A compound estimator for robust regression
16. Using robust scale estimates in detecting multiple outliers in linear regression
17. High breakdown-point and high efficiency robust estimates for regression