Two‐Stage Least Squares Analysis of Hierarchical Linear Models
Copyright policy )Two‐Stage Least Squares Analysis of Hierarchical Linear Models
AbstractThis paper has two major objectives. The first is to present a two‐stage least squares procedure for estimation of the parameters in a linear model whose parameters are in themselves linear functions of some hyperparameters. The second, and perhaps more important point, is that the new estimator can be shown to be generally more precise than either the Bayesian or the generalized single‐stage least squares estimator reported by LINDLEY and SMITH (1072).
- University of Nigeria Nigeria
hierarchical linear models, generalized single-stage least squares estimator, Bayesian inference, Analysis of variance and covariance (ANOVA), two- stage least squares procedure, hyperparameters, Bayesian estimates
hierarchical linear models, generalized single-stage least squares estimator, Bayesian inference, Analysis of variance and covariance (ANOVA), two- stage least squares procedure, hyperparameters, Bayesian estimates
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!