In previous work we introduced a construction to produce biorthogonal multiresolutions from given subdivisions. The approach involved estimating the solution to a least squares problem by means of a number of smaller least squares approximations on local portions of the data. In this work we use a result by Dahlquist, et. al. on the method of averages to make observational comparisons between this local least squares estimation and full least squares approximation. We have explored examples in two problem domains: data reduction and data approximation. We observe that, particularly for design matrices with a repetitive pattern of column entries, the least squares solution is often well estimated by local least squares, that the estimation r...
AbstractSurface interpolation finds application in many aspects of science and technology. Two speci...
Data gathering is a constant in human history with ever increasing amounts in quantity and dimension...
We review the development and extensions of the classical total least squares method and describe al...
If globally high dimensional data has locally only low dimensional distributions, it is advantageous...
Instead of minimizing the sum of all $n$ squared residuals as the classical least squares (LS) does,...
Abstract: We consider the problem of the least-squares approximation on two-dimensional un...
summary:The consistency of the least trimmed squares estimator (see Rousseeuw [Rous] or Hampel et al...
AbstractIt is a common procedure for scattered data approximation to use local polynomial fitting in...
Thesis (Ph.D.)--University of Washington, 2018We revisit and make progress on some old but challengi...
Local polynomial reproduction is a key ingredient in providing error estimates for several approxima...
In contrast to estimation by ordinary least squares, estimation by total least squares has much less...
In contrast to estimation by ordinary least squares, estimation by total least squares has much less...
Abstract. We show that the well-known least squares (LS) solution of an overdetermined system of lin...
Abstract. We describe two experiments recently conducted with the approximate moving least squares (...
Surface interpolation finds application in many aspects of science and technology. Two specific area...
AbstractSurface interpolation finds application in many aspects of science and technology. Two speci...
Data gathering is a constant in human history with ever increasing amounts in quantity and dimension...
We review the development and extensions of the classical total least squares method and describe al...
If globally high dimensional data has locally only low dimensional distributions, it is advantageous...
Instead of minimizing the sum of all $n$ squared residuals as the classical least squares (LS) does,...
Abstract: We consider the problem of the least-squares approximation on two-dimensional un...
summary:The consistency of the least trimmed squares estimator (see Rousseeuw [Rous] or Hampel et al...
AbstractIt is a common procedure for scattered data approximation to use local polynomial fitting in...
Thesis (Ph.D.)--University of Washington, 2018We revisit and make progress on some old but challengi...
Local polynomial reproduction is a key ingredient in providing error estimates for several approxima...
In contrast to estimation by ordinary least squares, estimation by total least squares has much less...
In contrast to estimation by ordinary least squares, estimation by total least squares has much less...
Abstract. We show that the well-known least squares (LS) solution of an overdetermined system of lin...
Abstract. We describe two experiments recently conducted with the approximate moving least squares (...
Surface interpolation finds application in many aspects of science and technology. Two specific area...
AbstractSurface interpolation finds application in many aspects of science and technology. Two speci...
Data gathering is a constant in human history with ever increasing amounts in quantity and dimension...
We review the development and extensions of the classical total least squares method and describe al...