Hostname: page-component-745bb68f8f-5r2nc Total loading time: 0 Render date: 2025-01-14T19:31:10.813Z Has data issue: false hasContentIssue false
  • English
  • Français

ASYMPTOTIC ROBUSTNESS IN MULTIPLE GROUP LINEAR-LATENT VARIABLE MODELS

Published online by Cambridge University Press:  16 May 2002

Albert Satorra
Albert Satorra
Affiliation:
Universitat Pompeu Fabra
Get access Rights & Permissions [Opens in a new window]

Abstract

Standard methods for analyzing linear-latent variable models rely on the assumption that the observed variables are normally distributed. Normality allows statistical inferences to be carried out based solely on the first-and second-order moments. In general, inferences for nonnormally distributed data require the estimates of matrices of third-and fourth-order moments. In the present paper, we show that inferences based on normal theory retain validity and asymptotic efficiency under general assumptions that allow for considerable departure from normality. In particular, we obtain conditions under which correct asymptotic inferences are attained when replacing a matrix of higher order moments by a matrix that depends only on cross-product moments of the data.

Type
Research Article
Information
Econometric Theory , Volume 18 , Issue 2 , April 2002 , pp. 297 - 312
Copyright
© 2002 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)