Maik Schwarz

Catholic University of Louvain, Walloon Region, Belgium

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Publications (2)0.53 Total impact

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    ABSTRACT: A new nonparametric estimator of production a frontier is defined and studied when the data set of production units is contaminated by measurement error. The measurement error is assumed to be an additive normal random variable on the input variable, but its variance is unknown. The estimator is a modification of the m-frontier, which necessitates the computation of a consistent estimator of the conditional survival function of the input variable given the output variable. In this paper, the identification and the consistency of a new estimator of the survival function is proved in the presence of additive noise with unknown variance. The performance of the estimator is also studied through simulated data.
    Institut d'�conomie Industrielle (IDEI), Toulouse, IDEI Working Papers. 01/2010;
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    Maik Schwarz, S�bastien Van Bellegem
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    ABSTRACT: We estimate the distribution of a real-valued random variable from contaminated observations. The additive error is supposed to be normally distributed, but with unknown variance. The distribution is identi able from the observations if we restrict the class of considered distributions by a simple condition in the time domain. A minimum distance estimator is shown to be consistent imposing only a slightly stronger assumption than the identification condition.
    Statistics [?] Probability Letters 01/2009; · 0.53 Impact Factor

Publication Stats

15 Citations
0.53 Total Impact Points


  • 2009
    • Catholic University of Louvain
      Walloon Region, Belgium