The latent budget model is a reduced rank model for the analysis of compositional data. The model can be also understood as a supervised neural network model with weights interpreted as conditional probabilities. Main advantage of this approach is that a classification rule for budget data can be defined for new observed cases. In this paper, a constrained (weighted) least-squares algorithm - which is alternative to the one already introduced in literature for standard latent budget model - is proposed for the estimation of the parameters. A distinction is made between conditional latent budget analysis (the standard approach) and unconditional latent budget analysis (the neural network approach).

Unconditional Latent Budget Analysis: a Neural Network Approach

SICILIANO, ROBERTA;
2001

Abstract

The latent budget model is a reduced rank model for the analysis of compositional data. The model can be also understood as a supervised neural network model with weights interpreted as conditional probabilities. Main advantage of this approach is that a classification rule for budget data can be defined for new observed cases. In this paper, a constrained (weighted) least-squares algorithm - which is alternative to the one already introduced in literature for standard latent budget model - is proposed for the estimation of the parameters. A distinction is made between conditional latent budget analysis (the standard approach) and unconditional latent budget analysis (the neural network approach).
978-3-642-59471-7
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/175728
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact