Feasible parameter set for linear models with bounded errors in all variables

Abstract When all observed variables of a model are affected by noise, parameter estimation is known as the errors in variables problem. While parameter bounding methods and algorithms have been extensively developed in the case of exactly known regressor variables, little attention has been paid to the bounded errors-in-variables problem. In this paper, a formal proof of a previous result on the description of the feasible parameter region for linear models in the presence of bounded errors in all variables is given. Topological features of the feasible parameter region, such as convexity and connectedness, are also discussed.

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