✦ LIBER ✦

Least squares cross-validation for the kernel deconvolution density estimator

✍ Scribed by Élie Youndjé; Martin T. Wells

Publisher: Elsevier Science
Year: 2002
Tongue: English
Weight: 50 KB
Volume: 334
Category: Article
ISSN: 1631-073X
DOI: 10.1016/s1631-073x(02)02291-4

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Asymptotic optimality of the least-squar

Asymptotic optimality of the least-squares cross-validation bandwidth for kernel estimates of intensity functions

✍ Maria Mori Brooks; J.Stephen Marron 📂 Article 📅 1991 🏛 Elsevier Science 🌐 English ⚖ 495 KB

Fast cross-validation algorithms for lea

Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression

✍ Senjian An; Wanquan Liu; Svetha Venkatesh 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 199 KB

A Cross-Validation Bandwidth Choice for

A Cross-Validation Bandwidth Choice for Kernel Density Estimates with Selection Biased Data

✍ Colin O. Wu 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 376 KB

This paper studies the risks and bandwidth choices of a kernel estimate of the underlying density when the data are obtained from s independent biased samples. The main results of this paper give the asymptotic representation of the integrated squared errors and the mean integrated squared errors of

On the non-consistency of the L2-cross-v

On the non-consistency of the L2-cross-validated kernel density estimate

✍ Luc Devroye 📂 Article 📅 1989 🏛 Elsevier Science 🌐 English ⚖ 583 KB

Partial least squares analysis with cros

Partial least squares analysis with cross-validation for the two-class problem: A Monte Carlo study

✍ Lars Ståhle; Svante Wold 📂 Article 📅 1987 🏛 John Wiley and Sons 🌐 English ⚖ 683 KB

On the asymptotic mean integrated square

On the asymptotic mean integrated squared error of a kernel density estimator for dependent data

✍ Jan Mielniczuk 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 357 KB

Hall and Hart (1990) proved that the mean integrated squared error (MISE) of a marginal kernel density estimator from an infinite moving average process X1, )(2 .... may be decomposed into the sum of MISE of the same kernel estimator for a random sample of the same size and a term proportional to th