Data boundary fitting using a generalized least-squares method

Scattered data fitting using least squares with interpolation method

Scattered data fitting is a big issue in numerical analysis. In many applications, some of the data are contaminated by noise and some are not. It is not appropriate to interpolate the noisy data, and the traditional least squares method may lose accuracy at the points which are not contaminated. In this paper, we present least squares with interpolation method to solve this problem. The existe...

Least Squares Fitting of Data

This is the usual introduction to least squares fit by a line when the data represents measurements where the y–component is assumed to be functionally dependent on the x–component. Given a set of samples {(xi, yi)}i=1, determine A and B so that the line y = Ax + B best fits the samples in the sense that the sum of the squared errors between the yi and the line values Axi + B is minimized. Note...

Hermite Scattered Data Fitting by the Penalized Least Squares Method

Given a set of scattered data with derivative values. If the data is noisy or there is an extremely large number of data, we use an extension of the penalized least squares method of von Golitschek and Schumaker [Serdica, 18 (2002), pp.1001-1020] to fit the data. We show that the extension of the penalized least squares method produces a unique spline to fit the data. Also we give the error bou...

PEDOMODELS FITTING WITH FUZZY LEAST SQUARES REGRESSION

Pedomodels have become a popular topic in soil science and environmentalresearch. They are predictive functions of certain soil properties based on other easily orcheaply measured properties. The common method for fitting pedomodels is to use classicalregression analysis, based on the assumptions of data crispness and deterministic relationsamong variables. In modeling natural systems such as s...

Least-Squares Fitting of Data with Polynomials