Title: Two-stage regression quantiles and two-stage trimmed least squares estimators for structural equation models
Authors: Chen, LA
Portnoy, S
Institute of Statistics
Keywords: linear model;structural equation model;regression quantile;trimmed least squares estimator
Issue Date: 1996
Abstract: We propose a two-stage trimmed least squares estimator for the parameters of structural equation model and provide the corresponding asymptotic distribution theory. The estimator is based on two-stage regression quantiles, which generalize the standard Linear model regression quantiles introduced by Koenker and Bassett (1978). The asymptotic theory is developed by means of ''Barhadur'' representations for the two-stage regression quantiles and the two-stage trimmed least squares estimator. The representations approximate these estimators as sums of independent random variables plus an additive term involving the first stage estimator. Asymptotic normal distributions are derived from these representations, and a simulation comparing some two-stage estimators is presented.
URI: http://hdl.handle.net/11536/1543
ISSN: 0361-0926
Volume: 25
Issue: 5
Begin Page: 1005
End Page: 1032
Appears in Collections:Articles