✦ LIBER ✦

Tests for location-scale families based on the empirical characteristic function

✍ Scribed by T.W. Epps

Publisher: Springer
Year: 2005
Tongue: English
Weight: 184 KB
Volume: 62
Category: Article
ISSN: 0026-1335
DOI: 10.1007/s001840400358

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Robust scale estimation based on the the

Robust scale estimation based on the the empirical characteristic function

✍ Marianthi Markatou; Joel L. Horowitz; Russell V. Lenth 📂 Article 📅 1995 🏛 Elsevier Science 🌐 English ⚖ 471 KB

Nonparametric Location Tests Based on th

Nonparametric Location Tests Based on the Empirical Distribution

✍ Doz. Dr. Juerg Huesler; Prof. Dr. Hans Riedwyl 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 427 KB 👁 2 views

We present a unified approach to the nonparametric cornparison of locations in the one-and two sample case using the empirical distribution functions. This approach reveals how to define a simple test statistic for this comparison in the case of large sample sizes which is equivalent to the known Wi

A note on scale estimates based on the e

A note on scale estimates based on the empirical characteristic function and their application to test for normality

✍ A.H. Welsh 📂 Article 📅 1984 🏛 Elsevier Science 🌐 English ⚖ 234 KB

A characterization and a class of omnibu

A characterization and a class of omnibus tests for the exponential distribution based on the empirical characteristic function

✍ N. Henze; S. G. Meintanis 📂 Article 📅 2010 🏛 Springer US 🌐 English ⚖ 230 KB

A consistent modification of a test for

A consistent modification of a test for independence based on the empirical characteristic function

✍ A. Kankainen; N. G. Ushakov 📂 Article 📅 1998 🏛 Springer US 🌐 English ⚖ 385 KB

Goodness-of-Fit Tests for a Multivariate

Goodness-of-Fit Tests for a Multivariate Distribution by the Empirical Characteristic Function

✍ Yanqin Fan 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 422 KB

In this paper, we take the characteristic function approach to goodness-of-fit tests. It has several advantages over existing methods: First, unlike the popular comparison density function approach suggested in Parzen (1979), our approach is applicable to both univariate and multivariate data; Secon