Choosing the number of factors in independent factor analysis model

Abstract

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 relevantissue 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 numberof mixture components must also be determined. This issue will be treated according to two main approaches. The first one amounts to carry out alikelihood ratio test. The other one is based on a penalized form of the likelihood, that leads to the so called information criteria. Some simulationsand empirical results on real data sets are finally presented.

Keywords

Faktorska analiza;Modeli;

Data

Language:	English
Year of publishing:	2005
Typology:	1.01 - Original Scientific Article
Organization:	UL FDV - Faculty of Social Sciences
Publisher:	Fakulteta za družbene vede
UDC:	303
COBISS:	24315485
ISSN:	1854-0023
Views:	259
Downloads:	62
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Unknown
Secondary keywords:	Factor analysis;Models;
URN:	URN:NBN:SI
Type (COBISS):	Not categorized
Pages:	str. 219-229
Volume:	ǂVol. ǂ2
Issue:	ǂno. ǂ2
Chronology:	2005
Keywords (UDC):	social sciences;družbene vede;methods of the social sciences;metode družbenih ved;
ID:	1467969