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; M. O. O. I. J. A. A. R. T., A.. - (2001), pp. 127-134. [10.1007/978-3-642-59471-1]
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).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.