Fitting a Cm-smooth function to data, III

Fitting a Cm - Smooth Function to Data

Suppose we are given a finite subset E ⊂ R and a function f : E → R. How to extend f to a C function F : R → R with C norm of the smallest possible order of magnitude? In this paper and in [20] we tackle this question from the perspective of theoretical computer science. We exhibit algorithms for constructing such an extension function F , and for computing the order of magnitude of its C norm....

PRELIMINARY VERSION Fitting a C-Smooth Function to Data

Fitting a C-Smooth Function to Data II

We exhibit efficient algorithms to perform the following task: Given a function f defined on a finite subset E ⊂ R, compute a C function F on R, with a controlled C norm, that approximates f on the subset E.

A Note on the Smooth Estimator of the Quantile Function with Left-Truncated Data

This note focuses on estimating the quantile function based on the kernel smooth estimator under a truncated dependent model. The Bahadurtype representation of the kernel smooth estimator is established, and from the Bahadur representation it can be seen that this estimator is strongly consistent.

Signal detection Using Rational Function Curve Fitting

In this manuscript, we proposed a new scheme in communication signal detection which is respect to the curve shape of received signal and based on the extraction of curve fitting (CF) features. This feature extraction technique is proposed for signal data classification in receiver. The proposed scheme is based on curve fitting and approximation of rational fraction coefficients. For each symbo...