This paper proposes a new robust regression model for interval-valued data based on midpoints and log-ranges of the dependent intervals, and obtains the parameter estimators using Huber loss function to deal with possible outliers in a data set. Besides, the use of logarithm transformation avoids...
Van Aelst & Willems (2005) developed the robust multivariate regression S-estimator for multivariate regression using real variables. Therefore, the multivariate regression model used in this study (eq. 9), which uses complex variables, is transformed into a corresponding real-valued model:...
(2016) are assumed to be either independent or inherently dependent via a linear regression model. Importantly, the proposed ambiguity sets are moment-based, but yield a confidence region for the mean (and possibly higher-order moments) of each uncertain parameter. Finally, some data-driven ...
A wide array of applications can be addressed through the proposed framework, including, among others, robust regression, robust detection and estimation, clustering in impulsive environments, spectrum sensing when signals are corrupted by heavy-tailed noise, and robust compressed sensing (CS) and recon...
As we describe in Section 4, in the absence of a specific parametric model for correlations within a trajectory, inference proceeds in a conservative manner by assuming arbitrarily high correlations within a trajectory (much like the clustered standard error adjustment in linear regression). Since ...
dataa_0=c(0,1),#return estimates for A = 0 and A = 1SL_Q="SL.npreg",#use kernel regression for E(Y | A = a, W)glm_g="W1 + W2",#use misspecified main terms glm for E(A | W)SL_Qr="SL.npreg",#use kernel regression to guard against#misspecification of outcome regression...
It is obvious that restricting the hyperparameter a in (11) to the interval (0, 1) ignores many alternative values for a and this is not a desired property for a chosen family of prior distributions. In contrast, in robust Bayes analysis, one can choose any value for the hyperparameter ...
[22] and compared with those of the above mentioned techniques. The number of regression classes in MLLR was set to 8 for the Aurora2 task and 16 for the KPOW task. In the MLLR-based model adaptation, we adopted the unsupervised adaptation method where the acoustic mean models were ...
Profiled side-channel attacks are understood to be powerful when applicable: in the best case when an adversary can comprehensively characterise the leakage, the resulting model leads to attacks requiring a minimal number of leakage traces for success. S
using methods from semi-definite programming, and the disadvantage that the dimension of the projectors is as large as the data now. this prohibits this approach for many applications in particular in image processing. the projector pca model is still non-convex, and a convex relaxation, calledre...