Results 21 to 30 of about 2,481,944 (297)
Behavior Research Methods, 2019 In the analysis of clustered or hierarchical data, a variety of statistical techniques can be applied. Most of these techniques have assumptions that are crucial to the validity of their outcome.
M. Deen, Mark de Rooij
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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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Posterior propriety of an objective prior for generalized hierarchical normal linear models
Statistical Theory and Related Fields, 2022 Bayesian Hierarchical models has been widely used in modern statistical application. To deal with the data having complex structures, we propose a generalized hierarchical normal linear (GHNL) model which accommodates arbitrarily many levels, usual ...
Cong Lin, Dongchu Sun, Chengyuan Song
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Journal of Research in Education Sciences, 2010 本研究目的在瞭解參與集中式資優班的國中資優生，其科學自我概念是否存在負向「大魚小池效應」（big fish little pond effect），抑或資優班的標籤反而帶給個人正向的榮耀感效應（reflected glory effects）？研究樣本包含367 位資優生及1,364位一般生，以科學自我概念量表、自然科學業成就測驗與班級榮耀感量表為工具，使用HLM 軟體進行學生及班級二階層模式（multilevel modeling）的統計分析。研究結論為：一、我國資優班學生的科學自我概念存在明顯的負向「
侯雅齡 Ya-Ling Hou
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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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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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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
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, 2015 Growth during early life history plays a key role in the recruitment dynamics of marine fishes; however, the effects of environmental stressors on growth are often difficult to quantify. In this study, increment widths from sagittal otoliths were used as
Brian K Gallagher +4 more
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Functional Plant Breeding Journal, 2020 This paper presents the state of the art of the statistical modelling as applied to plant breeding. Classes of inference, statistical models, estimation methods and model selection are emphasized in a practical way.
Marcos Deon Vilela de Resende +1 more
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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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