We present a polynomial multigrid method for the nodal interior penalty formulation of the Poisson equation on three-dimensional Cartesian grids. Its key ingredient is a weighted overlapping Schwarz smoother operating on element-centered subdomains. The MG method reaches superior convergence rates corresponding to residual reductions of about two orders of magnitude within a single V(1,1) cycle...

The application of geographically weighted regression (GWR) - a local spatial statistical technique used to test for spatial nonstationarity - has grown rapidly in the social, health and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity...

The least weighted squares estimator is a well known technique in robust regression. Its likelihood analogy in logistic regression is the maximum weighted likelihood estimator, proposed in Vandev and Neykov (1998) and Mueller and Neykov (2003). This article mentions already proved properties, shows its inconsistency and compare it to the other estimators by an extensive simulation. Introduction...

Parametric empirical Bayes PEB may perform poorly when the assumed prior distribution is seriously invalid Nonparametric empirical Bayes NEB is more robust since it imposes no restric tion on the prior But compared with the PEB the NEB may be ine cient for small to medium samples due to the large variation and under dispersion of the NPMLE of the prior Using Monte Carlo simulations we compare t...

In this article, we discuss wavelet versions of nonparametric curve estimators and weighted least squares wavelet regression. We introduce a modi ̄ed wavelet version of the Gasser-MÄ uller estimator that is unbiased whenever a wavelet representation of the nonparametric response curve correctly speci ̄es the response. In some cases, involving the Daubechies wavelet system, we ̄nd that the modi ̄ed ...

Reconstructing smooth curve from 2D noise sampled data is a very important issue in many fields, such as data processing, reverse engineering, and so on. In this paper, we present a novel method to reconstruct smooth curve approximating sampled data in plane, at the same time filtering noise in the data. Our method is constructed on a set of local piecewise polynomial approximations associated ...

We formulate optical flow estimation as a two-stage regression problem. Based on characteristics of these two regression models and conclusions on modern regression methods, we choose a Least Trimmed Squares followed by weighted Least Squares estimator to solve the optical flow constraint (OFC); and at places where this one-stage robust method fails due to poor derivative quality, we use a Leas...

This paper presents a robust hourly cooling-load forecasting method based on time-indexed autoregressive with exogenous inputs (ARX) models, in which the coefficients are estimated through a two-stage weighted least squares regression. The prediction method includes a combination of two separate time-indexed ARX models to improve prediction accuracy of the cooling load over different forecastin...

In this paper, we present a hybrid approach, robust principal component geographically weighted regression (RPCGWR), in examining the land change as a function of both extant urban land use and the effect of social and environmental factors in the Twin Cities Metropolitan Area (TCMA) of Minnesota. We used remotely sensed data to treat urban land use via the proxy of impervious surfaces. We then...