Tensor methods of full-information maximum likelihood estimation: Estimation with parameter constraints

DOI10.1007/BF01299734zbMath0838.62102OpenAlexW2076286467MaRDI QIDQ1905950

Seth A. Greenblatt

Publication date: 6 June 1996

Published in: Computational Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01299734

zbMATH Keywords

constrained optimizationtensor methodlog-likelihood functionfourth order approximationconstrained FIML estimatesfull-information maximum likelihood estimates

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Point estimation (62F10) Parametric inference under constraints (62F30) Numerical optimization and variational techniques (65K10) Numerical approximation and computational geometry (primarily algorithms) (65D99) Probabilistic methods, stochastic differential equations (65C99)

Cites Work

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• A new polynomial-time algorithm for linear programming
• Tensor methods for full-information maximum likelihood estimation: Unconstrained estimation
• Tensor Methods for Nonlinear Equations
• Tensor Methods for Unconstrained Optimization Using Second Derivatives
• Variational methods for the solution of problems of equilibrium and vibrations