“‘Variance Components’ Tag”,2020-05-12 (psychology)⁠:

Bibliography for tag statistics/variance-component, most recent first: 31 annotations & 4 links (parent).

[page summary]

• See Also
• Gwern
  • “Everything Is Correlated”, Gwern2014
  • “Variance Components Beyond Genetics”, Gwern2019
• Links
  • “MegaBayesianAlphabet: Mega-Scale Bayesian Regression Methods for Genome-Wide Prediction and Association Studies With Thousands of Traits”, Qu et al 2022
  • “Do Multiple Experimenters Improve the Reproducibility of Animal Studies?”, Kortzfleisch et al 2022
  • “Identifying Imaging Genetic Associations via Regional Morphometricity Estimation”, Bao et al 2022
  • “Interest of Phenomic Prediction As an Alternative to Genomic Prediction in Grapevine”, Brault et al 2021
  • “Raman2RNA: Live-Cell Label-Free Prediction of Single-Cell RNA Expression Profiles by Raman Microscopy”, Kobayashi-Kirschvink et al 2021
  • “Autism-Related Dietary Preferences Mediate Autism-Gut Microbiome Associations”, Yap et al 2021
  • “General Dimensions of Human Brain Morphometry Inferred from Genome-Wide Association Data”, Fürtjes et al 2021
  • “MegaLMM: Mega-Scale Linear Mixed Models for Genomic Predictions With Thousands of Traits”, Runcie et al 2021
  • “Small Effects: The Indispensable Foundation for a Cumulative Psychological Science”, Götz et al 2021
  • “A Parsimonious Model for Mass-Univariate Vertex-Wise Analysis”, Couvy-Duchesne et al 2021
  • “A Contamination Theory of the Obesity Epidemic”, Ludwin-Peery & Ludwin-Peery2021
  • “Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge”, Couvy-Duchesne et al 2020
  • “Exploring the Variance in Complex Traits Captured by DNA Methylation Assays”, Battram et al 2020
  • “A Unified Framework for Association and Prediction from Vertex-Wise Grey-Matter Structure”, Couvy-Duchesne et al 2020
  • “Using High-Throughput Phenotypes to Enable Genomic Selection by Inferring Genotypes”, Whalen et al 2020
  • “Analysis of Variance When Both Input and Output Sets Are High-Dimensional”, Campos et al 2020
  • “In-Field Whole Plant Maize Architecture Characterized by Latent Space Phenotyping”, Gage et al 2019
  • “Widespread Associations between Grey Matter Structure and the Human Phenome”, Couvy-Duchesne et al 2019
  • “Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies”, Ubbens et al 2019
  • “Predicting Human Inhibitory Control from Brain Structural MRI”, He et al 2019
  • “Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior”, Li et al 2019
  • “Phenomic Selection: a Low-Cost and High-Throughput Alternative to Genomic Selection”, Rincent et al 2018
  • “Intact Connectional Morphometricity Learning Using Multi-View Morphological Brain Networks With Application to Autism Spectrum Disorder”, Bessadok & Rekik2018
  • “The Relationship between Spatial Configuration and Functional Connectivity of Brain Regions”, Bijsterbosch et al 2018
  • “Environmental Factors Dominate over Host Genetics in Shaping Human Gut Microbiota Composition”, Rothschild et al 2017
  • “Morphometricity As a Measure of the Neuroanatomical Signature of a Trait”, Sabuncu et al 2016
  • “The Remarkable, yet Not Extraordinary, Human Brain As a Scaled-Up Primate Brain and Its Associated Cost”, Herculano-Houzel2012
  • “Mapping the Human Exposome: It’s Now Possible to Map a Person’s Lifetime Exposure to Nutrition, Bacteria, Viruses, and Environmental Toxins-Which Profoundly Influence Human Health”
  • “Playing around With ‘Gendermetricity’”
  • “Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation”
  • “Enhanced Cerebral Blood Flow Similarity of the Somatomotor Network in Chronic Insomnia: Transcriptomic Decoding, Gut Microbial Signatures and Phenotypic Roles”
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