16 resultados para Multilevel Linear Models
Filtro por publicador
- Aberdeen University (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (2)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (6)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (1)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Archimer: Archive de l'Institut francais de recherche pour l'exploitation de la mer (1)
- Aston University Research Archive (12)
- Biblioteca de Teses e Dissertações da USP (2)
- Biblioteca Digital | Sistema Integrado de Documentación | UNCuyo - UNCUYO. UNIVERSIDAD NACIONAL DE CUYO. (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (28)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (79)
- Biblioteca Virtual del Sistema Sanitario Público de Andalucía (BV-SSPA), Junta de Andalucía. Consejería de Salud y Bienestar Social, Spain (6)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (48)
- Brock University, Canada (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (9)
- CentAUR: Central Archive University of Reading - UK (94)
- Cochin University of Science & Technology (CUSAT), India (2)
- Coffee Science - Universidade Federal de Lavras (2)
- Collection Of Biostatistics Research Archive (16)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (3)
- Consorci de Serveis Universitaris de Catalunya (CSUC), Spain (56)
- Cor-Ciencia - Acuerdo de Bibliotecas Universitarias de Córdoba (ABUC), Argentina (1)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (1)
- Dalarna University College Electronic Archive (14)
- Digital Commons at Florida International University (4)
- DigitalCommons@The Texas Medical Center (19)
- DigitalCommons@University of Nebraska - Lincoln (1)
- Diposit Digital de la UB - Universidade de Barcelona (1)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (15)
- Duke University (3)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (2)
- Glasgow Theses Service (3)
- Institutional Repository of Leibniz University Hannover (1)
- Instituto Nacional de Saúde de Portugal (1)
- Instituto Politécnico do Porto, Portugal (8)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (1)
- Lume - Repositório Digital da Universidade Federal do Rio Grande do Sul (1)
- Martin Luther Universitat Halle Wittenberg, Germany (1)
- Massachusetts Institute of Technology (4)
- Ministerio de Cultura, Spain (1)
- National Center for Biotechnology Information - NCBI (2)
- Nottingham eTheses (1)
- Portal do Conhecimento - Ministerio do Ensino Superior Ciencia e Inovacao, Cape Verde (1)
- Publishing Network for Geoscientific & Environmental Data (5)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (2)
- Repositório Aberto da Universidade Aberta de Portugal (2)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (2)
- Repositório Científico da Universidade de Évora - Portugal (11)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (7)
- Repositório da Produção Científica e Intelectual da Unicamp (4)
- Repositorio de la Universidad de Cuenca (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (12)
- Repositório do Centro Hospitalar de Lisboa Central, EPE - Centro Hospitalar de Lisboa Central, EPE, Portugal (1)
- Repositorio Institucional da UFLA (RIUFLA) (1)
- Repositório Institucional da Universidade de Brasília (2)
- Repositório Institucional da Universidade Estadual de São Paulo - UNESP (2)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (115)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (11)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- Savoirs UdeS : plateforme de diffusion de la production intellectuelle de l’Université de Sherbrooke - Canada (1)
- Scielo España (1)
- Scielo Saúde Pública - SP (33)
- Universidad de Alicante (2)
- Universidad del Rosario, Colombia (8)
- Universidad Politécnica de Madrid (12)
- Universidade Complutense de Madrid (2)
- Universidade do Minho (4)
- Universidade dos Açores - Portugal (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (6)
- Universidade Técnica de Lisboa (2)
- Universitat de Girona, Spain (12)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (8)
- Université de Lausanne, Switzerland (125)
- Université de Montréal (1)
- Université de Montréal, Canada (25)
- University of Canberra Research Repository - Australia (1)
- University of Connecticut - USA (2)
- University of Michigan (2)
- University of Queensland eSpace - Australia (23)
- University of Southampton, United Kingdom (2)
- University of Washington (5)
Resumo:
Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when either the number of classes or the cluster size is large. We propose a maximum pairwise likelihood (MPL) approach via a modified EM algorithm for this case. We also show that a simple latent class analysis, combined with robust standard errors, provides another consistent, robust, but less efficient inferential procedure. Simulation studies suggest that the three methods work well in finite samples, and that the MPL estimates often enjoy comparable precision as the ML estimates. We apply our methods to the analysis of comorbid symptoms in the Obsessive Compulsive Disorder study. Our models' random effects structure has more straightforward interpretation than those of competing methods, thus should usefully augment tools available for latent class analysis of multilevel data.