Sensitivity of principal Hessian direction analysis
, 2007 We provide sensitivity comparisons for two competing versions of the dimension reduction method principal Hessian directions (pHd). These comparisons consider the effects of small perturbations on the estimation of the dimension reduction subspace via ...
Prendergast, Luke A., Smith, Jodie A.
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Regularized robust estimation in binary regression models. [PDF]
J Appl Stat, 2022 Tang Q, Karunamuni RJ, Liu B.
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Modified maximum likelihood estimator under the Jones and Faddy's skew t-error distribution for censored regression model. [PDF]
J Appl Stat, 2021 Acitas S +3 more
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Robust Statistical Inference in Generalized Linear Models Based on Minimum Renyi's Pseudodistance Estimators. [PDF]
Entropy (Basel), 2022 Jaenada M, Pardo L.
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Multiresponse Robust Engineering: Case with Errors in Factor Levels [PDF]
, 2009 2000 Mathematics Subject Classification: 62J05, 62J10, 62F35, 62H12, 62P30.The model-based robust approach for improving the quality of the process is successfully applied to different industrial processes.
Koleva, Elena +2 more
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Robust fitting of mixtures of GLMs by weighted likelihood. [PDF]
Adv Stat Anal, 2022 Greco L.
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An automatic robust Bayesian approach to principal component regression. [PDF]
J Appl Stat, 2021 Gagnon P, Bédard M, Desgagné A.
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About the Concept of Weights of WLTE(k) Estimators [PDF]
, 2003 2002 Mathematics Subject Classification: 62F35, 62F15.The concept for trimming and weighing the terms in the Method of Maximum Likelihood gives us a very flexible and useful way to improve the robustness of MLE.
Atanasov, Dimitar
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High breakdown estimators for principal components: the projection-pursuit approach revisited. [PDF]
Li and Chen (J. Amer. Statist. Assoc. 80 (1985) 759) proposed a method for principal components using projection-pursuit techniques. In classical principal components one searches for directions with maximal variance, and their approach consists of ...
Croux, Christophe, Ruiz-Gazen, A
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Variable selection in finite mixture of regression models using the skew-normal distribution. [PDF]
J Appl Stat, 2020 Yin J, Wu L, Dai L.
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