Robust Estimation in Dependent Situations

Vision Based Robust Pose Estimation System in Different Observed Situations for an Outdoor Quadrotor

In this paper, we present a vision based robust pose estimation system in different observed situations for a quadrotor in outdoor environments. This system could provide us with approximate ground truth of pose estimation for an outdoor quadrotor, while most of existing vision based systems perform indoors. We only use the own features of the quadrotor, while most existing systems modify the a...

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

Robust normative comparisons of socially risky situations

In this paper, we theoretically characterize robust empirically implementable normative criteria for evaluating socially risky situations. Socially risky situations are modeled as distributions, among individuals, of lotteries on a finite set of statecontingent pecuniary consequences. Individuals are assumed to have selfish Von Neumann-Morgenstern preferences for these socially risky situations...

Robust Estimation via Robust Gradient Estimation

We provide a new computationally-efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models: in the classical Huber ǫ-contamination model and in heavy-tailed settings. Our workhorse is a novel robust variant of gradient descent, and we provide conditions under which our gradient descent variant provides accurate estimators in a g...

Robust Covariance Matrix Estimation with Data-Dependent VAR Prewhitening Order