On estimation of measurement error models with replication under heavy-tailed distributions

Jin-Guan Lin () and Chun-Zheng Cao

Computational Statistics, 2013, vol. 28, issue 2, 809-829

Abstract: Measurement error (errors-in-variables) models are frequently used in various scientific fields, such as engineering, medicine, chemistry, etc. In this work, we consider a new replicated structural measurement error model in which the replicated observations jointly follow scale mixtures of normal (SMN) distributions. Maximum likelihood estimates are computed via an EM type algorithm method. A closed expression is presented for the asymptotic covariance matrix of those estimators. The SMN measurement error model provides an appealing robust alternative to the usual model based on normal distributions. The results of simulation studies and a real data set analysis confirm the robustness of SMN measurement error model. Copyright Springer-Verlag 2013

Keywords: EM algorithm; Measurement error; Replicated measurement; Scale mixtures of normal distribution (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s00180-012-0330-4

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