1 resultado para Two-stage stochastic model
em Dalarna University College Electronic Archive
Filtro por publicador
- Aberdeen University (1)
- Academic Archive On-line (Stockholm University; Sweden) (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (8)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (1)
- Aquatic Commons (11)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (16)
- Aston University Research Archive (47)
- Avian Conservation and Ecology - Eletronic Cientific Hournal - Écologie et conservation des oiseaux: (1)
- B-Digital - Universidade Fernando Pessoa - Portugal (1)
- Biblioteca de Teses e Dissertações da USP (2)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (15)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (10)
- Biblioteca Digital de la Universidad Católica Argentina (1)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (8)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (19)
- Boston University Digital Common (1)
- Brock University, Canada (1)
- Bucknell University Digital Commons - Pensilvania - USA (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (5)
- CaltechTHESIS (5)
- Cambridge University Engineering Department Publications Database (33)
- CentAUR: Central Archive University of Reading - UK (32)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (62)
- Cochin University of Science & Technology (CUSAT), India (3)
- Coffee Science - Universidade Federal de Lavras (2)
- Collection Of Biostatistics Research Archive (4)
- CORA - Cork Open Research Archive - University College Cork - Ireland (2)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (9)
- CUNY Academic Works (1)
- Dalarna University College Electronic Archive (1)
- Deakin Research Online - Australia (40)
- Department of Computer Science E-Repository - King's College London, Strand, London (1)
- Digital Commons - Michigan Tech (6)
- Digital Commons at Florida International University (3)
- Digital Repository at Iowa State University (1)
- DigitalCommons@The Texas Medical Center (16)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (1)
- DRUM (Digital Repository at the University of Maryland) (4)
- Duke University (7)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (5)
- FUNDAJ - Fundação Joaquim Nabuco (1)
- Greenwich Academic Literature Archive - UK (9)
- Helda - Digital Repository of University of Helsinki (7)
- Illinois Digital Environment for Access to Learning and Scholarship Repository (3)
- Indian Institute of Science - Bangalore - Índia (133)
- INSTITUTO DE PESQUISAS ENERGÉTICAS E NUCLEARES (IPEN) - Repositório Digital da Produção Técnico Científica - BibliotecaTerezine Arantes Ferra (1)
- Instituto Politécnico do Porto, Portugal (1)
- Massachusetts Institute of Technology (2)
- Ministerio de Cultura, Spain (1)
- National Center for Biotechnology Information - NCBI (3)
- Nottingham eTheses (1)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (1)
- Publishing Network for Geoscientific & Environmental Data (13)
- QSpace: Queen's University - Canada (5)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (43)
- Queensland University of Technology - ePrints Archive (139)
- Repositório Científico da Universidade de Évora - Portugal (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (14)
- Repositório Institucional da Universidade de Aveiro - Portugal (2)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (55)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (2)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- Universidad de Alicante (3)
- Universidad del Rosario, Colombia (8)
- Universidad Politécnica de Madrid (15)
- Universidade Complutense de Madrid (1)
- Universidade Federal do Pará (1)
- Universitat de Girona, Spain (3)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (2)
- Université de Lausanne, Switzerland (4)
- Université de Montréal (2)
- Université de Montréal, Canada (7)
- University of Connecticut - USA (1)
- University of Michigan (7)
- University of Queensland eSpace - Australia (19)
- University of Washington (3)
- WestminsterResearch - UK (1)
Resumo:
A number of recent works have introduced statistical methods for detecting genetic loci that affect phenotypic variability, which we refer to as variability-controlling quantitative trait loci (vQTL). These are genetic variants whose allelic state predicts how much phenotype values will vary about their expected means. Such loci are of great potential interest in both human and non-human genetic studies, one reason being that a detected vQTL could represent a previously undetected interaction with other genes or environmental factors. The simultaneous publication of these new methods in different journals has in many cases precluded opportunity for comparison. We survey some of these methods, the respective trade-offs they imply, and the connections between them. The methods fall into three main groups: classical non-parametric, fully parametric, and semi-parametric two-stage approximations. Choosing between alternatives involves balancing the need for robustness, flexibility, and speed. For each method, we identify important assumptions and limitations, including those of practical importance, such as their scope for including covariates and random effects. We show in simulations that both parametric methods and their semi-parametric approximations can give elevated false positive rates when they ignore mean-variance relationships intrinsic to the data generation process. We conclude that choice of method depends on the trait distribution, the need to include non-genetic covariates, and the population size and structure, coupled with a critical evaluation of how these fit with the assumptions of the statistical model.