2026-04-17T15:45:57Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/12352025-12-23T02:15:08Zcom_2072_6col_2072_452953
RECERCAT
author
Satorra, Albert
2017-07-26T12:07:57Z2017-07-26T12:07:57Z1996-10-012017-07-23T02:02:42Z
We consider the application of normal theory methods to the
estimation and testing of a general type of multivariate regression
models with errors--in--variables, in the case where various data sets
are merged into a single analysis and the observable variables deviate
possibly from normality. The various samples to be merged can differ on
the set of observable variables available. We show that there is a
convenient way to parameterize the model so that, despite the possible
non--normality of the data, normal--theory methods yield correct inferences
for the parameters of interest and for the goodness--of--fit test. The
theory described encompasses both the functional and structural model
cases, and can be implemented using standard software for structural
equations models, such as LISREL, EQS, LISCOMP, among others. An
illustration with Monte Carlo data is presented.
L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess
asymptotic robustness
multivariate regression
asymptotic efficiency
normal theory methods
multi--samples
errors--in--variables
Statistics, Econometrics and Quantitative Methods
Fusion of data sets in multivariate linear regression with errors-in-variables
info:eu-repo/semantics/workingPaper