Digitale Bibliotheek
Sluiten Bladeren door artikelen uit een tijdschrift
 
<< vorige    volgende >>
     Tijdschrift beschrijving
       Alle jaargangen van het bijbehorende tijdschrift
         Alle afleveringen van het bijbehorende jaargang
           Alle artikelen van de bijbehorende aflevering
                                       Details van artikel 4 van 7 gevonden artikelen
 
 
  A neural network approach to normality testing
 
 
Titel: A neural network approach to normality testing
Auteur: Sigut, J.
Piñeiro, J.
Estévez, J.
Toledo, P.
Verschenen in: Intelligent data analysis
Paginering: Jaargang 10 (2006) nr. 6 pagina's 509-519
Jaar: 2006-11-27
Inhoud: The aim of this work is to provide a new approach to the classical problem of determining whether or not a set of data has been sampled from a univariate normal distribution. A simple neural network architecture is proposed as an efficient way of combining and reinforcing the discriminatory capabilities of different popular statistics commonly used in conventional hypothesis testing procedures. Special emphasis is placed on the fact that these procedures lack a reliable measure of the degree to which the observed data supports the normality assumption. Several authors have shown that the so-called P-values are inefficient and ambiguous when dealing with this matter, so the Bayesian posterior probabilities are suggested as the best candidates to play this role. For this reason, a significant part of our work has focused on training the neural networks so that their outputs accurately approximate these probabilities.
Uitgever: IOS Press
Bronbestand: Elektronische Wetenschappelijke Tijdschriften
 
 

                             Details van artikel 4 van 7 gevonden artikelen
 
<< vorige    volgende >>
 
 Koninklijke Bibliotheek - Nationale Bibliotheek van Nederland