Hypothesis testing in the presence of nuisance parameters

DOI10.1016/0378-3758(95)00048-8zbMath0848.62060OpenAlexW2048871344MaRDI QIDQ1918150

Maxwell L. King

Publication date: 18 July 1996

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

Full work available at URL: https://ageconsearch.umn.edu/record/267419/files/monash-161.pdf

zbMATH Keywords

tablesMonte Carlonuisance parametersmarginal likelihood\(p\)-valuesfirst-order autoregressive disturbancesfirst-order autocorrelationlinear regression coefficientsdynamic linear regression model

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Parametric hypothesis testing (62F03)

Related Items

Unnamed Item, Modified Wald test for regression disturbances, On solving bias‐corrected non‐linear estimation equations with an application to the dynamic linear model, Testing for a trend with persistent errors, Marginal-likelihood score-based tests of regression disturbances in the presence of nuisance parameters, Using point optimal test of a simple null hypothesis for testing a composite null hypothesis via maximized Monte Carlo approach

Cites Work

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• On the impact of the tests for serial correlation upon the test of significance for the regression coefficient
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• A Maximum Likelihood Procedure for Regression with Autocorrelated Errors
• Marginal likelihood methods for distributed lag models
• Alternative Tests of the Error Components Model
• Estimation of autoregressive parameters from a marginal likelihood function
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