Maximum likelihood computation based on the Fisher scoring and Gauss-Newton quadratic approximations

DOI10.1016/J.CSDA.2006.12.037zbMath1161.62331OpenAlexW1992698788MaRDI QIDQ1020014

Yong Wang

Publication date: 29 May 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.12.037

zbMATH Keywords

constrained optimizationFisher scoringGauss-Newton methoditeratively reweighted least-squares

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Point estimation (62F10) Parametric inference under constraints (62F30)

Related Items (3)

The constrained Fisher scoring method for maximum likelihood computation of a nonparametric mixing distribution ⋮ Fisher scoring: an interpolation family and its Monte Carlo implementations ⋮ Minimum disparity computation via the iteratively reweighted least integrated squares algorithms

Uses Software

• R
• MASS (R)

Cites Work

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• A constrained formulation of maximum-likelihood estimation for normal mixture distributions
• Statistical Analysis of Financial Data in S-Plus
• An Empirical Distribution Function for Sampling with Incomplete Information
• Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model
• Estimation and testing in constrained covariance component models
• Finite mixture models
• Maximum likelihood estimation of ordered multinomial parameters

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