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Consistent estimation of regression coefficients in ultrastructural measurement error model using stochastic prior information

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Abstract

The problem of consistent estimation of regression coefficients in a multivariate linear ultrastructural measurement error model is considered in this article when some additional information on regression coefficients is available a priori. Such additional information is expressible in the form of stochastic linear restrictions. Utilizing stochastic restrictions given a priori, some methodologies are presented to obtain the consistent estimators of regression coefficients under two types of additional information separately, viz., covariance matrix of measurement errors and reliability matrix associated with explanatory variables. The measurement errors are assumed to be not necessarily normally distributed. The asymptotic properties of the proposed estimators are derived and analyzed analytically as well as numerically through a Monte Carlo simulation experiment.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Kanpur, 208 016, India

    Shalabh & Neeraj Misra

  2. Department of Mathematics, Jaypee University of Information Technology, Waknaghat, 173 215, Solan, Himachal Pradesh, India

    Gaurav Garg

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  1. Shalabh
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  2. Gaurav Garg
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  3. Neeraj Misra
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Correspondence to Shalabh.

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Shalabh, Garg, G. & Misra, N. Consistent estimation of regression coefficients in ultrastructural measurement error model using stochastic prior information. Stat Papers 51, 717–748 (2010). https://doi.org/10.1007/s00362-008-0162-z

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  • DOI: https://doi.org/10.1007/s00362-008-0162-z

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