Likelihood Inference for Linear Regression Models
Copyright policy )Likelihood Inference for Linear Regression Models
A linear regression model with p regression coefficients \(\beta_ j\) \((j=1,...,p
- Stanford University United States
asymptotic expansion, Linear regression; mixed models, Bartlett adjustment, approximation error, chi- squared approximation of the conditional distribution, location-scale analysis of Darwin's data, Point estimation, large-sample likelihood ratio methods, restricted ML estimator, conditional inference, log-likelihood-ratio statistic, Parametric hypothesis testing, confidence limit, unconstrained ML estimator, nuisance parameters, maximum likelihood, conditional mean and variance
asymptotic expansion, Linear regression; mixed models, Bartlett adjustment, approximation error, chi- squared approximation of the conditional distribution, location-scale analysis of Darwin's data, Point estimation, large-sample likelihood ratio methods, restricted ML estimator, conditional inference, log-likelihood-ratio statistic, Parametric hypothesis testing, confidence limit, unconstrained ML estimator, nuisance parameters, maximum likelihood, conditional mean and variance
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