This paper proposes fuzzy regression analysis with non-symmetric fuzzy coefficients. By assuming non-symmetric triangular fuzzy coefficients and applying the quadratic programming formulation, the center of the obtained fuzzy regression model attains more central tendency compared to the one with symmetric triangular fuzzy coefficients. For a data set composed of crisp inputs-fuzzy outputs, two...

Fuzzy regression models has been traditionally considered as a problem of linear programming. We introduce new models founded on quadratic programming with the aim of overcoming the limitations of linear programming, and that allow to define a great amplitude of wide variety. We verify the existence of multicollinearity in fuzzy regression and we propose a model based on Ridge regression in ord...

The paper deals with the asymptotic distribution of the least squares estimator of a change point in a regression model where the regression function has two phases — the first linear and the second quadratic. In the case when the linear coefficient after change is non-zero the limit distribution of the change point estimator is normal whereas it is non-normal if the linear coefficient is zero.

This paper presents the interval estimate for specific points in polynomial regression: zero of a linear regression, abscissa of the extreme of a quadratic regression, abscissa of the inflection point of a cubic regression. Two different approaches are under study. An application of these two approaches based on quadratic regression in presented: interval estimate for the plant density giving o...

We experiment with several chunking models. Deeper architectures achieve better generalization. Quadratic filters, a simplification of a theoretical model of V1 complex cells, reliably increase accuracy. In fact, logistic regression with quadratic filters outperforms a standard single hidden layer neural network. Adding quadratic filters to logistic regression is almost as effective as feature ...

The interaction between linear, quadratic programming and regression analysis are explored by both statistical and operations research methods. Estimation and optimization problems are formulated in two different ways: on one hand linear and quadratic programming problems are formulated and solved by statistical methods, and on the other hand the solution of the linear regression model with con...