Erratum: Fitting a $C^m$-smooth function to data II

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....

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.

PRELIMINARY VERSION Fitting a C-Smooth Function to Data

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.

Erratum: Hybrid Deterministic-Stochastic Methods for Data Fitting

∗Submitted to the journal’s Computational Methods in Science and Engineering section February 1, 2013; accepted for publication March 25, 2013; published electronically August 30, 2013. http://www.siam.org/journals/sisc/35-4/90825.html †Department of Computer Science, University of British Columbia, Vancouver V6T 1Z4, BC, Canada ([email protected]). ‡INRIA-SIERRA team, Laboratoire d’Informatique de...