Robust Statistical Procedures

By Philippe Huber. 1977

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Citations in the corpus

Truncated product method for combining P‐values (2002, background): “By truncating, these large components are removed, thereby providing more power, much like a " trimmed mean " gains efficiency in the presence of outliers [Huber, 1977].”
Bootstrapping Phylogenetic Trees: Theory and Methods (2003, background): “The more precise study of influence functions (Huber, 1996; Hampel, Ronchetti, Rousseeuw and Stahel, 1986) is not available here because a notion of derivative in T is unavailable.”
Improved fast gauss transform and efficient kernel density estimation (2003, methodology): “Many approaches in computer vision and pattern recognition use kernel density estimation, including support vector machines [23], M -estimation [18], normalized cut [24] and mean shift analysis [5].”
Mean Shift: A Robust Approach Toward Feature Space Analysis (2002, unknown): “See [26], [32] for introductory surveys.”
Highly Robust Estimation of the Autocovariance Function (2000, background): “Keywords. autocovariance, breakdown point, in¯uence function, robustness, scale estimation.”
LADE-Based Inference for ARMA Models With Unspecified and Heavy-Tailed Heteroscedastic Noises (2015, methodology): “The self-weighting approach can be applied for the M- and self-weighted M-estimators, see Huber (1977), He et al. (1990), and references therein for the classical M-estimation.”
Parametric (modified least squares) and non-parametric (Theil–Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors (2005, background): “One exception is the family of R-Estimators based on rank-ordered statistics (Huber, 1996).”