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Tensor Methods for Full-Information Maximum Likelihood Estimation: Unconstrained Estimation

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  • Greenblatt, Seth A

Abstract

In this study, we present a new method, called a tensor method, for the computation of unconstrained Full-Information Maximum Likelihood (FIML) estimates. The new technique is based upon a fourth order approximation to the log-likelihood function, rather than the second order approximation used in standard methods. The higher order terms are low rank third and fourth order tensors that are computed, at very little storage or computation cost, using information from previous iterations. We form and solve the tensor model, then present test results showing that the tensor method is far more efficient than the standard Newton's method for a wide range of unconstrained FIML estimation problems. Citation Copyright 1994 by Kluwer Academic Publishers.

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  • Greenblatt, Seth A, 1994. "Tensor Methods for Full-Information Maximum Likelihood Estimation: Unconstrained Estimation," Computational Economics, Springer;Society for Computational Economics, vol. 7(2), pages 89-108.
    • Handle: RePEc:kap:compec:v:7:y:1994:i:2:p:89-108
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