Influential factors of school bullying among junior high school students: an analysis based on hierarchical linear models [PDF]
Frontiers in PsychologyIntroductionExploring the factors and internal mechanisms influencing school bullying among junior high school students is crucial for preventing and controlling its occurrence.
Yingying Wang +6 more
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Hierarchical Generalized Linear Models: The R Package HGLMMM [PDF]
Journal of Statistical Software, 2011 The R package HGLMMM has been developed to fit generalized linear models with random effects using the h-likelihood approach. The response variable is allowed to follow a binomial, Poisson, Gaussian or gamma distribution.
Marek Molas, Emmanuel Lesaffre
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Enhancing insight into regional differences: hierarchical linear models in multiregional clinical trials [PDF]
BMC Medical Research MethodologyBackground The planning and analysis of multi-regional clinical trials (MRCTs) has increased in the pharmaceutical industry to facilitate global research and development.
Jeewuan Kim, Seung-Ho Kang
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Insight into Genome-Wide Associations of Growth Trajectories Using a Hierarchical Non-Linear Mixed Model [PDF]
BiologyIn applying a hierarchical mixed model to genome-wide association analysis (GWAS) of longitudinal data, dimensionality reduction through modeling repeated measurements improves both computational efficiency and statistical power. Legendre polynomials can
Ying Zhang +3 more
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A conjugate prior for discrete hierarchical log-linear models
Annals of Statistics, 2009 In Bayesian analysis of multi-way contingency tables, the selection of a prior distribution for either the log-linear parameters or the cell probabilities parameters is a major challenge. In this paper, we define a flexible family of conjugate priors for
Dobra, Adrian +2 more
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Hierarchical Generalized Linear Models
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1996 SUMMARY We consider hierarchical generalized linear models which allow extra error components in the linear predictors of generalized linear models. The distribution of these components is not restricted to be normal; this allows a broader class of models, which includes generalized linear mixed models.
Y. Lee, J. A. Nelder
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Albatross analytics a hands-on into practice: statistical and data science application
Journal of Big Data, 2022 Albatross Analytics is a statistical and data science data processing platform that researchers can use in disciplines of various fields. Albatross Analytics makes it easy to implement fundamental analysis for various regressions with random model ...
Rezzy Eko Caraka +7 more
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Shrinkage estimates for multi-level heteroscedastic hierarchical normal linear models [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 Empirical Bayes approach is an attractive method for estimating hyperparameters in hierarchical models. But, under the assumption of normality for a multi-level heteroscedastic hierarchical model, which involves several explanatory variables, the analyst
S.K. Ghoreishi, A. Mostafavinia
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Bayesian analysis of hierarchical heteroscedastic linear models using Dirichlet-Laplace priors [PDF]
Journal of Statistical Theory and Applications (JSTA), 2017 From practical point of view, in a two-level hierarchical model, the variance of second-level usually has a tendency to change through sub-populations.
S. K. Ghoreishi
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Math and science outcomes for students of teachers from standard and alternative pathways in Texas
Education Policy Analysis Archives, 2020 Texas provides a unique opportunity to examine teachers without standard university preparation, for it prepares more teachers through alternative pathways than any other state.
Michael Marder +2 more
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