Robust regression and outlier detection
Robust regression and outlier detection
Computational Statistics & Data Analysis
New algorithms for computing the least trimmed squares regression estimator
Computational Statistics & Data Analysis
Parallel algorithms for computing all possible subset regression models using the QR decomposition
Parallel Computing - Special issue: Parallel computing in numerical optimization
Computational methods for modifying seemingly unrelated regressions models
Journal of Computational and Applied Mathematics - Special issue: Proceedings of the international conference on linear algebra and arithmetic, Rabat, Morocco, 28-31 May 2001
Computing LTS Regression for Large Data Sets
Data Mining and Knowledge Discovery
Computationally efficient methods for estimating the updated-observations SUR models
Applied Numerical Mathematics
The multivariate least-trimmed squares estimator
Journal of Multivariate Analysis
Seemingly unrelated regression model with unequal size observations: computational aspects
Computational Statistics & Data Analysis
Editorial: 3rd Special issue on matrix computations and statistics
Computational Statistics & Data Analysis
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An algorithm for computing the exact least trimmed squares (LTS) estimator of the standard regression model has recently been proposed. The LTS algorithm is adapted to the general linear and seemingly unrelated regressions models with possible singular dispersion matrices. It searches through a regression tree to find the optimal estimates and has combinatorial complexity. The model is formulated as a generalized linear least squares problem. Efficient matrix techniques are employed to update the generalized residual sum of squares of a subset model. Specifically, the new algorithm utilizes previous computations to update a generalized QR decomposition by a single row. The sparse structure of the model is exploited. Theoretical measures of computational complexity are provided. Experimental results confirm the ability of the new algorithms to identify outlying observations.