- Referenced in 88 articles
- using the IDA algorithm , Standard and robust estimation of the equivalence class of a Directed...
- Referenced in 68 articles
- robust scatter matrix such as the minimum covariance determinant or an S-estimator ... combines projection pursuit ideas with robust scatter matrix estimation. ROBPCA yields more accurate ... estimates at noncontaminated datasets and more robust estimates at contaminated data. ROBPCA can be computed...
- Referenced in 42 articles
- package rrcov: Scalable Robust Estimators with High Breakdown Point. Robust Location and Scatter Estimation...
- Referenced in 604 articles
- velocities at the interface. The stability and robustness of the HLLE solver is closely related ... restores the missing Rarefaction wave by some estimates, like linearisations, these can be simple ... middle wave speed. They are quite robust and efficient but somewhat more diffusive...
- Referenced in 64 articles
- model predictive control, state and parameter estimation and robust optimization. ACADO Toolkit is implemented...
- Referenced in 31 articles
- package sandwich: Robust Covariance Matrix Estimators , Model-robust standard error estimators for cross-sectional, time...
- Referenced in 99 articles
- Missing value estimation for DNA microarrays. Motivation: Gene expression microarray experiments can generate data sets ... clustering and K-means clustering are not robust to missing data, and may lose effectiveness ... this report, we investigate automated methods for estimating missing data. Results: We present a comparative ... provide a more robust and sensitive method for missing value estimation than SVDimpute, and both...
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- package ROptEst: Optimally robust estimation , Optimally robust estimation in general smoothly parameterized models using...
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- estimation of a simultaneous equation model by maximum likelihood and optimal B-robust methods ... presented and discussed in the paper: Robust estimators for simultaneous equations models. This paper presents ... class of robust estimators for linear and non-linear simultaneous equations models, which ... construction of an optimal robust estimator which is the best trade-off between efficiency...
- Referenced in 110 articles
- Thirdly, a robust algorithm for ”border matting” has been developed to estimate simultaneously the alpha...
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- package contains code that implements the robust estimators discussed in the recent second edition...
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- robCompositions: Robust Estimation for Compositional Data. The package includes methods for imputation of compositional data...
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- RTDE: Robust Tail Dependence Estimation. Robust tail dependence estimation for bivariate models. This package ... papers by the authors:’Robust and bias-corrected estimation of the coefficient of tail dependence ... Robust and bias-corrected estimation of extreme failure sets’. This work was supported...
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- package covRobust: Robust Covariance Estimation via Nearest Neighbor Cleaning. The cov.nnve() function for robust covariance...
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- dependence scaling and to provide reliable local estimates of the Hurst exponents. The tool, which ... spectrum is introduced. It yields robust local or global estimates of the Hurst parameter that...
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- robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. As any real ... makes it difficult to spot contamination. Robust estimation methods aim to solve both problems ... effects model estimation in R. The robust estimation method in robustlmm is based ... crossed) grouping structures. The robustness of the estimates and their asymptotic efficiency is fully controlled...
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- this kind we develop robust and inexpensive estimates of both the local error...
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- packages for the computation of optimally robust estimators and tests as well as the necessary...
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- program for solving least squares and robust estimation problems. GaussFit is written in C computer ... numerical differentiation. GaussFit provides robust estimation...
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- robust algorithm for discrete tomography from limited projection data with automated gray value estimation ... with automated gray value estimation. This algorithm is more robust and automated than the original ... gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments ... society with an easy-to-use and robust algorithm...