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Modeling Human Morphological Competence
Frontiers in Psychology, 2020 One of the central debates in the cognitive science of language has revolved around the nature of human linguistic competence. Whether syntactic competence should be characterized by abstract hierarchical structures or reduced to surface linear strings ...
Yohei Oseki +4 more
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HLMdiag: A Suite of Diagnostics for Hierarchical Linear Models in R
Journal of Statistical Software, 2014 Over the last twenty years there have been numerous developments in diagnostic pro- cedures for hierarchical linear models; however, these procedures are not widely imple- mented in statistical software packages, and those packages that do contain a ...
Adam Loy, Heike Hofmann
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Physical Review Physics Education Research, 2019 [This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] Physics education researchers (PER) often analyze student data with single-level regression models (e.g., linear and logistic regression).
Ben Van Dusen, Jayson Nissen
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Ecology and Evolution, 2022 Biological data are often intrinsically hierarchical (e.g., species from different genera, plants within different mountain regions), which made mixed‐effects models a common analysis tool in ecology and evolution because they can account for the non ...
Johannes Oberpriller +2 more
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Bayesian designs for hierarchical linear models [PDF]
Statistica Sinica, 2009 Summary: Two Bayesian optimal design criteria for hierarchical linear models are discussed: the \(\psi_\beta\) criterion for the estimation of individual-level parameters \(\beta\), and the \(\psi_\theta\) criterion for the estimation of hyperparameters \(\mathbf \theta\).
Liu, Qing +2 more
openaire +3 more sources
Hierarchical generalized linear models for multiple groups of rare and common variants: jointly estimating group and individual-variant effects. [PDF]
PLoS Genetics, 2011 Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are ...
Nengjun Yi +3 more
doaj +1 more source
Deep Gaussian Mixture Models [PDF]
, 2017 Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed.
McLachlan, Geoffrey J., Viroli, Cinzia
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Prior distributions for objective Bayesian analysis [PDF]
, 2018 We provide a review of prior distributions for objective Bayesian analysis. We start by examining some foundational issues and then organize our exposition into priors for: i) estimation or prediction; ii) model selection; iii) highdimensional models ...
Consonni, Guido +3 more
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Model-based clustering via linear cluster-weighted models
, 2015 A novel family of twelve mixture models with random covariates, nested in the linear $t$ cluster-weighted model (CWM), is introduced for model-based clustering.
Aitken +38 more
core +1 more source
The distribution of supermassive black holes in the nuclei of nearby galaxies [PDF]
, 1998 The growth of supermassive black holes by merging and accretion in hierarchical models of galaxy formation is studied by means of Monte Carlo simulations.
Cattaneo, A. +4 more
core +4 more sources