A framework is set up in which linear regression, as a way of approximating a random variable by other random variables, can be carried out in a variety of ways, which moreover ca...
R. Tyrrell Rockafellar, Stan Uryasev, Michael Zaba...
The new concept and method of imposing imprecise (fuzzy) input and output data upon the conventional linear regression model is proposed in this paper. We introduce the fuzzy scala...
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
— This work addresses the problem of selecting a subset of basis functions for a model linear in the parameters for regression tasks. Basis functions from a set of candidates are...
A new method is proposed to estimate the nonlinear functions in an additive regression model. Usually, these functions are estimated by penalized least squares, penalizing the cur...