Calzolari, Giorgio and Neri, Laura (2002): Imputation of continuous variables missing at random using the method of simulated scores. Published in: Compstat 2002, Proceedings in Computational Statistics, 15th Symposium held in Berlin No. Ed. by W. Haerdle and B. Roenz. Heidelberg: Physika Verlag (2002): pp. 389394.

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Abstract
For multivariate datasets with missing values, we present a procedure of statistical inference and state its "optimal" properties. Two main assumptions are needed: (1) data are missing at random (MAR); (2) the data generating process is a multivariate normal linear regression. Disentangling the problem of convergence of the iterative estimation/imputation procedure, we show that the estimator is a "method of simulated scores" (a particular case of McFadden's "method of simulated moments"); thus the estimator is equivalent to maximum likelihood if the number of replications is conveniently large, and the whole procedure can be considered an optimal parametric technique for imputation of missing data.
Item Type:  MPRA Paper 

Original Title:  Imputation of continuous variables missing at random using the method of simulated scores 
Language:  English 
Keywords:  Simulates scores; missing data; estimation/imputation; structural form; reduced form 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General 
Item ID:  22986 
Depositing User:  Giorgio Calzolari 
Date Deposited:  04 Jun 2010 20:12 
Last Modified:  28 Sep 2019 23:00 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/22986 