Go to the content. | Move to the navigation | Go to the site search | Go to the menu | Contacts | Accessibility

| Create Account

Pace, L. and Salvan, A. and Ventura, L. (2003) Likelihood based discrimination between separate scale and regression models. [Working Paper] WORKING PAPER SERIES, 16/2003 . , PADOVA (Inedito)

Full text disponibile come:

[img]
Preview
PDF Document
33Mb

Abstract (english)

The aim of this paper is to compare through simulation the likelihood ratio (LR) test with the most powerful invariant (MPI) test, and approximations thereof, for discriminating between two separate scale and regression models. The LR test as well as the approximate (first order) MPI test based on the leading term of the Laplace expansion for integrals are easy to compute. They only require the maximum likelihood estimates for the regression and scale parameters and the two observed informations. Even the approximate (second order) MPI test is not computationally heavy. On the contrary, the exact MPI test is expressed in terms of multidimensional integrals whose numerical evaluation appears reliable only in the two-dimensional and in the three-dimensional case. Two conclusions emerge in this paper. First, for scale and location models, exact (when computable) and approximate MPI tests are equivalent to the LR test in all the situations considered and for every sample size. This contrasts somehow with the prescription usually implied in the literature. Second, when the dimension of the regression parameter is a considerable fraction of a small or moderate sample size, the second order approximation to the MPI test clearly improves on the LR test, unlike the first order approximation.


Statistiche Download - Aggiungi a RefWorks
EPrint type:Working Paper
Anno di Pubblicazione:September 2003
Key Words:Laplace expansion, likelihood ratio test, marginal likelihood, model selection, modified profile likelihood, most powerful invariant test, profile likelihood, scale and regression model.
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:7309
Depositato il:12 Dec 2014 10:33
Simple Metadata
Full Metadata
EndNote Format

Download statistics

Solo per lo Staff dell Archivio: Modifica questo record