How entropic regression beats the outliers problem in nonlinear system identification
نویسندگان
چکیده
منابع مشابه
Bayesian identification of clustered outliers in multiple regression
We propose a Bayesian model for clustered outliers in multiple regression. In the literature, outliers are frequently modeled as coming from a subgroup where the variance of the errors is much larger than in the rest of the data. By contrast, when a cluster of outliers exists, we show that it can be more informative to model them as coming from a subgroup where different regression coefficients...
متن کاملUnsupervised Regression with Applications to Nonlinear System Identification
We derive a cost functional for estimating the inverse of the observation function in nonlinear dynamical systems. Limiting our search to invertible observation functions confers numerous benefits, including a compact representation and no local minima. Our approximation algorithms for optimizing this cost functional are fast, and give diagnostic bounds on the quality of their solution. Our met...
متن کاملThe Hausdorff entropic moment problem
Our aim in this paper is twofold. First, to find the necessary and sufficient conditions to be satisfied by a given sequence of real numbers $vn%n50 ` to represent the ‘‘entropic moments’’ * [0,a]@r(x)# dx of an unknown non-negative, decreasing and differentiable ~a.e.! density function r(x) with a finite interval support. These moments are called entropic moments because they are closely conne...
متن کاملOutliers robustness in multivariate orthogonal regression
This paper deals with the problem of multivariate affine regression in the presence of outliers in the data. The method discussed is based on weighted orthogonal least squares. The weights associated with the data satisfy a suitable optimality criterion and are computed by a two-step algorithm requiring a RANSAC step and a gradient-based optimization step. Issues related to the breakdown point ...
متن کاملNonlinear Cointegrating Regression under Weak Identification
An asymptotic theory is developed for a weakly identified cointegrating regression model in which the regressor is a nonlinear transformation of an integrated process. Weak identification arises from the presence of a loading coefficient for the nonlinear function that may be close to zero. In that case, standard nonlinear cointegrating limit theory does not provide good approximations to the f...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Chaos: An Interdisciplinary Journal of Nonlinear Science
سال: 2020
ISSN: 1054-1500,1089-7682
DOI: 10.1063/1.5133386