Uniform error estimates for artificial neural network approximations for heat equations

Using Artificial Neural Network Approximations Testing for structural breaks in nonlinear dynamic models using artificial neural network approximations

In this paper we suggest a number of statistical tests based on neural network models, that are designed to be powerful against structural breaks in otherwise stationary time series processes while allowing for a variety of nonlinear specifications for the dynamic model underlying them. It is clear that in the presence of nonlinearity standard tests of structural breaks for linear models may no...

Lp error estimates for projection approximations

We provide an error estimate for the local mean projection approximation in L p([0, τ∗]) for p ∈ [1,+∞[, in terms of the regularity of the underlying grid, and we apply it to the corresponding projection approximation of weakly singular Fredholm integral equations of the second kind. © 2004 Elsevier Ltd. All rights reserved.

Error estimates for approximations from control nets

Artificial Neural Network Model for Predicting Insurance Insolvency

In addition to its primary role of providing financial protection for other industries the insurance industry also serves as a medium for fund mobilization. In spite of the harsh economic environment in Nigeria, the insurance industry has been crucial to the consummation of business plans and wealth creation.  However, the continued downturn experienced by many countries, in the last decade, se...

Uniform Schauder Estimates for Regularized Hypoelliptic Equations

In this paper we are concerned with a family of elliptic operators represented as sum of square vector fields: L = ∑m i=1X 2 i + ∆ in Rn, where ∆ is the Laplace operator, m < n, and the limit operator L = ∑m i=1X 2 i is hypoelliptic. Here we establish Schauder’s estimates, uniform with respect to the parameter , of solution of the approximated equation L u = f , using a modification of the lift...