Rank-Based Inference in Linear Models with Stable Errors
24 Pages Posted: 3 Jun 2010
Date Written: June 1, 2010
Abstract
Linear models with stable error densities are considered. The local asymptotic normality of the resulting model is established. We use this result, combined with Le~Cam's third lemma, to obtain local powers of various classical rank tests (Wilcoxon's and van der Waerden's test, the median test, and their counterparts for regression and analysis of variance) under stable laws. The same results are used to construct new rank tests achieving parametric optimality at specified stable densities. A Monte-Carlo study is conducted to compare their relative performances.
Keywords: Stable distributions, local asymptotic normality, rank tests, asymptotic relative efficiencies
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Estimation of Continuous-Time Processes via the Empirical Characteristic Function
By George J. Jiang and John Knight
-
Empirical Characteristic Function Estimation and its Applications
By Jun Yu
-
Empirical Characteristic Function in Time Series Estimation
By John Knight and Jun Yu
-
Efficient Estimation Using the Characteristic Function
By Marine Carrasco and Rachidi Kotchoni
-
By Marine Carrasco, Mikhail Chernov, ...
-
Estimation of Stable Distributions by Indirect Inference
By René Garcia, Eric Renault, ...
-
Cross Validated Snp Density Estimates
By Mark Coppejans and A. Ronald Gallant
-
The Method of Simulated Quantiles
By Yves Dominicy and David Veredas
-
Indirect Estimation of Elliptical Stable Distributions
By Marco J. Lombardi and David Veredas