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Regression analysis with dichotomous regressors and misclassification
Author(s) -
Bekker P.A.,
Montfort K.,
Mooijaart A.
Publication year - 1991
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.1991.tb01298.x
Subject(s) - estimator , mathematics , moment (physics) , statistics , regression analysis , monte carlo method , regression , identification (biology) , basis (linear algebra) , maximum likelihood , econometrics , botany , physics , geometry , classical mechanics , biology
We discuss a regression model in which the regressors are dummy variables. The basic idea is that the observation units can be assigned to some well‐defined combination of treatments, corresponding to the dummy variables. This assignment can not be done without some error, i.e. misclassification can play a role. This situation is analogous to regression with errors in variables. It is well‐known that in these situations identification of the parameters is a prominent problem. We will first show that, in our case, the parameters are not identified by the first two moments but can be identified by the likelihood. Then we analyze two estimators. The first is a moment estimator involving moments up to the third order, and the second is a maximum likelihood estimator calculated with the help of the EM algorithm. Both estimators are evaluated on the basis of a small Monte Carlo experiment.