Please use this identifier to cite or link to this item: https://repositorio.insper.edu.br/handle/11224/5942
Type: Working Paper
Title: Parsimonious Bayesian Factor Analysis when the Number of Factors is Unknown
Author: Fruhwirth-Schnatter, Sylvia
Lopes, Hedibert Freitas
Publication Date: 2014
Abstract: We introduce a new and general set of identifiability conditions for factor models which handles the ordering problem associated with current common practice. In addition, the new class of parsimonious Bayesian factor analysis leads to a factor loading matrix repre sentation which is an intuitive and easy to implement factor selection scheme. We argue that the structuring the factor loadings matrix is in concordance with recent trends in applied factor analysis. Our MCMC scheme for posterior inference makes several improvements over the existing alternatives while outlining various strategies for conditional posterior in ference in a factor selection scenario. Four applications, two based on synthetic data and two based on well known real data, are introduced to illustrate the applicability and gener ality of the new class of parsimonious factor models, as well as to highlight features of the proposed sampling schemes.
Keywords (english terms): Identifiability
parsimony
Cholesky decomposition
rank defficiency
Heywood problem
Language: Inglês
CNPq Area: Ciências Exatas e da Terra
Copyright: O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DO USUÁRIO VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR
Appears in Collections:Coleção Insper Working Papers

Files in This Item:
File Description SizeFormat 
2014_wpe345.pdf2014_wpe345466.13 kBAdobe PDFThumbnail
View/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.