1 resultado para upper and middle class
em Collection Of Biostatistics Research Archive
Filtro por publicador
- KUPS-Datenbank - Universität zu Köln - Kölner UniversitätsPublikationsServer (1)
- Repository Napier (1)
- Academic Research Repository at Institute of Developing Economies (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 (5)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (1)
- Andina Digital - Repositorio UASB-Digital - Universidade Andina Simón Bolívar (3)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (3)
- Archive of European Integration (2)
- Aston University Research Archive (13)
- Biblioteca de Teses e Dissertações da USP (1)
- Biblioteca Digital | Sistema Integrado de Documentación | UNCuyo - UNCUYO. UNIVERSIDAD NACIONAL DE CUYO. (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (10)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (56)
- Biblioteca Virtual del Sistema Sanitario Público de Andalucía (BV-SSPA), Junta de Andalucía. Consejería de Salud y Bienestar Social, Spain (1)
- Biodiversity Heritage Library, United States (4)
- Bioline International (1)
- Blue Tiger Commons - Lincoln University - USA (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (29)
- Brock University, Canada (16)
- Bucknell University Digital Commons - Pensilvania - USA (4)
- Bulgarian Digital Mathematics Library at IMI-BAS (4)
- CentAUR: Central Archive University of Reading - UK (54)
- Cochin University of Science & Technology (CUSAT), India (4)
- Collection Of Biostatistics Research Archive (1)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (12)
- Consorci de Serveis Universitaris de Catalunya (CSUC), Spain (14)
- Cor-Ciencia - Acuerdo de Bibliotecas Universitarias de Córdoba (ABUC), Argentina (1)
- CUNY Academic Works (1)
- Dalarna University College Electronic Archive (3)
- Department of Computer Science E-Repository - King's College London, Strand, London (1)
- Digital Archives@Colby (4)
- Digital Commons - Michigan Tech (3)
- Digital Commons at Florida International University (10)
- Digital Howard @ Howard University | Howard University Research (1)
- DigitalCommons - The University of Maine Research (1)
- DigitalCommons@The Texas Medical Center (2)
- DigitalCommons@University of Nebraska - Lincoln (1)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (21)
- DRUM (Digital Repository at the University of Maryland) (2)
- Duke University (1)
- Ecology and Society (1)
- Fachlicher Dokumentenserver Paedagogik/Erziehungswissenschaften (1)
- Glasgow Theses Service (1)
- Harvard University (6)
- Illinois Digital Environment for Access to Learning and Scholarship Repository (2)
- Institute of Public Health in Ireland, Ireland (3)
- Instituto Politécnico do Porto, Portugal (5)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (7)
- Lume - Repositório Digital da Universidade Federal do Rio Grande do Sul (1)
- Massachusetts Institute of Technology (1)
- Memoria Académica - FaHCE, UNLP - Argentina (4)
- Ministerio de Cultura, Spain (2)
- National Center for Biotechnology Information - NCBI (7)
- Nottingham eTheses (2)
- Portal de Revistas Científicas Complutenses - Espanha (1)
- Publishing Network for Geoscientific & Environmental Data (68)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (1)
- ReCiL - Repositório Científico Lusófona - Grupo Lusófona, Portugal (1)
- Repositório Científico da Universidade de Évora - Portugal (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (10)
- Repositório da Produção Científica e Intelectual da Unicamp (9)
- Repositório da Universidade Federal do Espírito Santo (UFES), Brazil (2)
- Repositório digital da Fundação Getúlio Vargas - FGV (17)
- Repositório Digital da UNIVERSIDADE DA MADEIRA - Portugal (1)
- Repositório do Centro Hospitalar de Lisboa Central, EPE - Centro Hospitalar de Lisboa Central, EPE, Portugal (2)
- Repositório Institucional da Universidade Federal do Rio Grande do Norte (1)
- Repositorio Institucional de la Universidad de Málaga (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (96)
- Research Open Access Repository of the University of East London. (1)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (16)
- School of Medicine, Washington University, United States (2)
- Scielo España (1)
- Scielo Saúde Pública - SP (60)
- Scientific Open-access Literature Archive and Repository (1)
- Scottish Institute for Research in Economics (SIRE) (SIRE), United Kingdom (2)
- Universidad Autónoma de Nuevo León, Mexico (1)
- Universidad de Alicante (1)
- Universidad del Rosario, Colombia (15)
- Universidad Politécnica de Madrid (3)
- Universidade Complutense de Madrid (1)
- Universidade do Minho (1)
- Universidade Federal do Pará (4)
- Universidade Federal do Rio Grande do Norte (UFRN) (10)
- Universidade Metodista de São Paulo (1)
- Universitat de Girona, Spain (1)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (6)
- Université de Lausanne, Switzerland (76)
- Université de Montréal, Canada (17)
- University of Michigan (55)
- University of Queensland eSpace - Australia (33)
- University of Washington (4)
- WestminsterResearch - UK (2)
Resumo:
Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.