Least-squares Two-point Function Estimation

Least-Squares Estimation of Transformation Parameters Between Two Point Patterns

Quality point cloud normal estimation by guided least squares representation

In this paper, we present a quality point cloud normal estimation method via subspace segmentation based on guided least squares representation. A structure guided low-rank subspace segmentation model has been employed in normal estimation (LRRSGNE). In order to select a consistent sub-neighborhood for a point, the subspace segmentation model is adopted to analyze the underlying structure of it...

Least Squares Estimation

Recursive Least Squares Estimation

We start with estimation of a constant based on several noisy measurements. Suppose we have a resistor but do not know its resistance. So we measure it several times using a cheap (and noisy) multimeter. How do we come up with a good estimate of the resistance based on these noisy measurements? More formally, suppose x = (x1, x2, . . . , xn) T is a constant but unknown vector, and y = (y1, y2, ...

Nonlinear Least-squares Estimation

The paper uses empirical process techniques to study the asymptotics of the least-squares estimator for the fitting of a nonlinear regression function. By combining and extending ideas of Wu and Van de Geer, it establishes new consistency and central limit theorems that hold under only second moment assumptions on the errors. An application to a delicate example of Wu’s illustrates the use of t...