Universita' di Firenze, Dipartimento di Statistica "G. Parenti", Working Paper 2010/01. Abstract: Given a set of continuous variables with missing data, we prove in this paper that the iterative application of a simple ``least-squares estimation/multivariate normal simulation'' procedure produces an efficient parameters estimator. There are two main assumptions behind our proof: (1) the missing data mechanism is ignorable; (2) the data generating process is a multivariate normal linear regression. Disentangling the iterative procedure and its convergence conditions, we show that the estimator is a ''method of simulated scores'' (a particular case of McFadden's ''method of simulated moments''), thus equivalent to maximum likelihood if the number of replications is conveniently large. We thus provide a non-Bayesian re-interpretation of the estimation/simulation problem. The computational procedure is obtained introducing a simple modification into existing algorithms. Its software implementation is straightforward (few simple statements in any programming language) and easily applicable to datasets with large number of variables.
The method of simulated scores for estimating multinormalregression models with missing values / G.Calzolari; L.Neri. - STAMPA. - (2010), pp. 1-27.
The method of simulated scores for estimating multinormalregression models with missing values
CALZOLARI, GIORGIO;
2010
Abstract
Universita' di Firenze, Dipartimento di Statistica "G. Parenti", Working Paper 2010/01. Abstract: Given a set of continuous variables with missing data, we prove in this paper that the iterative application of a simple ``least-squares estimation/multivariate normal simulation'' procedure produces an efficient parameters estimator. There are two main assumptions behind our proof: (1) the missing data mechanism is ignorable; (2) the data generating process is a multivariate normal linear regression. Disentangling the iterative procedure and its convergence conditions, we show that the estimator is a ''method of simulated scores'' (a particular case of McFadden's ''method of simulated moments''), thus equivalent to maximum likelihood if the number of replications is conveniently large. We thus provide a non-Bayesian re-interpretation of the estimation/simulation problem. The computational procedure is obtained introducing a simple modification into existing algorithms. Its software implementation is straightforward (few simple statements in any programming language) and easily applicable to datasets with large number of variables.File | Dimensione | Formato | |
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