In this paper, we describe a meshless approach to solve singularly perturbed differentialdifference equations of the second order with boundary layer at one end of the interval. In the numerical treatment for such type of problems, first we approximate the terms containing negative and positive shifts which converts it to a singularly perturbed differential equation. Next, a numerical scheme ba...

This paper addresses the problem of identifying echelon canonical forms for a vector autoregressive moving average model with exogenous variables using finite algorithms. For given values of the Kronecker indices a method for estimating the structural parameters of a model using ordinary least squares calculations is presented. These procedures give rise, rather naturally, to a technique for th...

Based on sampling likelihood and feature intensity, in this paper, a feature-preserving denoising algorithm for point-sampled surfaces is proposed. In terms of moving least squares surface, the sampling likelihood for each point on point-sampled surfaces is computed, which measures the probability that a 3D point is located on the sampled surface. Based on the normal tensor voting, the feature ...

We present an algorithm for approximating a function defined over $d$-dimensional manifold utilizing only noisy values at locations sampled from the with noise. To produce approximation we do not require any knowledge regarding other than its dimension $d$. use Manifold Moving Least-Squares approach of (Sober and Levin 2016) to reconstruct atlas charts is built on-top those charts. The resultin...

A finite strain element (FE)-based approach to element-free Galerkin (EFG) discretization is introduced, based on a number of simplifications and specialized techniques in the context Lagrangian kernel. In terms discretization, quadratic polynomial basis used, support determined from pre-assigned nodes for each quadrature point points coincide with centroids tetrahedra. Diffuse derivatives are ...

The difference based estimation in partially linear models is an approach designed to estimate parametric component by using the ordinary least squares estimator after removing the nonparametric component from the model by differencing. However, it is known that least squares estimates do not provide useful information for the majority of data when the error distribution is not normal, particul...