Análise de dados amostrais complexos utilizando redes neurais

The fitting of an Artificial Neural Network (ANN) considers the data coming from an simple random sample with replacement. However, in practice the selection of simple random samples for surveys is rarely used and more complex sampling schemes are used. The complex sampling schemes reflect complex structures from population. These structures of sampling scheme need to be incorporated when we fitting an ANN. In statistical literature, there are different aproaches for modelling data from complex surveys. However in the literature related to ANN there is no mention of how proceed when the data come from complex sample surveys. This work porpose an superpopulation approach for modelling data from complex survey using ANN. An evaluation of the proposed methodology is carried out empirically through simulations and, after, estimation measures are calculated. The practical application is done using data from the National Household Sample Survey for the year 2014. The objective is to improve the estimate of the per capita household income used to construct the Poverty Map in IBGE (2008).

Grantor dc:publisher

Universidade Federal do Rio de Janeiro

Year dc:date.issued

2017

Author dc:creator

• Pereira, Savano Sousa

Advisor dc:contributor.advisor

• Calôba, Luiz Pereira

• Engenharia elétrica
• Redes Neurais Artificiais
• Abordagem de superpopulação

Statement dc:rights

• Acesso Aberto

Language dc:language

por

Handle dc:identifier.uri

http://hdl.handle.net/11422/6335

OAI identifier oai:identifier

oai:pantheon.ufrj.br:11422/6335