34 KB178 KB24 KB200420242005Viroli, Cinzia11 straništevilka:2letnik:2str. 219-229ISSN:1854-0023ISSN:1854-0031COBISSID:24315485URN:URN:NBN:SI:doc-DM6U8FK7enFakulteta za družbene vedeMetodološki zvezkiFactor analysisFaktorska analizaModeliModelsstatistična analizastatistične metodeChoosing the number of factors in independent factor analysis model|Independent Factor Analysis (IFA) has recently been proposed in the signal processing literature as a way to model a set of observed variables through linear combinations of hidden independent ones plus a noise term. Despite the peculiarity of its origin the method can be framed within the latent variable model domain and some parallels with the ordinary Factor Analysis can be drawn. If no prior information on the latent structure is available a relevant issue concerns the correct specification of the model. In this work some methods to detect the number of significant latent variables are investigated. Moreover, since the method defines a probability density function for the latent variables by mixtures of gaussians, the correct number of mixture components must also be determined. This issue will be treated according to two main approaches. The first one amounts to carry out a likelihood ratio test. The other one is based on a penalized form of the likelihood, that leads to the so called information criteria. Some simulations and empirical results on real data sets are finally presentedTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za družbene vede