An adjusted least squares estimator is derived that yields a consistent estimate of the parameters of an implicit quadratic measurement error model. In addition, a consistent estimator for the measurement error noise variance is proposed. Important assumptions are: (1) all errors are uncorrelated identically distributed and (2) the error distribution is normal. The estimators for the quadratic ...

In this paper conditional parametric ARX-models are suggested and studied by simulation. These non-linear models are traditional ARXmodels in which the parameters are replaced by smooth functions. The estimation method is based on the ideas of locally weighted regression. It is demonstrated that kernel estimates (local constants) are in general inferior to local quadratic estimates. For the con...

Support vector machines (SVMs) are an extremely successful class of classification and regression algorithms. Building an SVM entails the solution of a constrained convex quadratic programming problem which is quadratic in the number of training samples. Previous parallel implementations of SVM solvers sequentially solved subsets of the complete problem, which is problematic when the solution r...

In this paper the problem of blind equalization of constant modulus (CM) signals is formulated within the support vector (SV) regression framework. The quadratic inequalities derived from the CM property are transformed into linear ones, thus yielding a quadratic programming (QP) problem. Then an iterative reweighted procedure is proposed to blindly restore the CM property. The technique can be...

in this paper, we solve the quadratic $alpha$-functional equations $2f(x) + 2f(y) = f(x + y) + alpha^{-2}f(alpha(x-y)); (0.1)$ where $alpha$ is a fixed non-archimedean number with $alpha^{-2}neq 3$. using the fixed point method and the direct method, we prove the hyers-ulam stability of the quadratic $alpha$-functional equation (0.1) in non-archimedean banach spaces.

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described in the first part of this presentation: (i) locally weighted regression (LOESS), (ii) additive model...