Parameter estimation in semi-linear models using a maximal invariant likelihood function

DOI10.1016/j.jspi.2008.07.011zbMath1153.62027OpenAlexW1982119703MaRDI QIDQ998984

Maxwell L. King, Jahar Lal Bhowmik

Publication date: 30 January 2009

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2005/wp18-05.pdf

zbMATH Keywords

tablesmaximum likelihood estimationtwo-step estimationnonlinear modellingsimulation experiment

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Point estimation (62F10) General nonlinear regression (62J02)

Related Items (2)

Deriving tests of the semi-linear regression model using the density function of a maximal invariant ⋮ A maximum likelihood method for the incidental parameter problem

Uses Software

• GAUSS

Cites Work

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• Marginal-likelihood score-based tests of regression disturbances in the presence of nuisance parameters
• Estimation and testing of regression disturbances based on modified likelihood functions
• Maximal invariant likelihood based testing of semi-linear models
• On a formula for the distribution of the maximum likelihood estimator
• Nonlinear Regression
• Marginal likelihood methods for distributed lag models
• Maximum Likelihood Estimation of Regression Models With Stochastic Trend Components
• Data Transformations and the Linear Model
• Consistent Estimates Based on Partially Consistent Observations

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