{"id":{"repo_id":"brazil-uerj","oai_identifier":"oai:pantheon.ufrj.br:11422/6335"},"canonical_url":"https://search.dev.ndltd.org/etd/brazil-uerj/oai:pantheon.ufrj.br:11422/6335","repository":{"repo_id":"brazil-uerj","name":"Brazil UERJ","base_url":"https://pantheon.ufrj.br/oai/request"},"display":{"title":"Análise de dados amostrais complexos utilizando redes neurais","abstract":"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).","abstract_html":"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).","abstract_has_math":false,"creators":["Pereira, Savano Sousa"],"institution":"Universidade Federal do Rio de Janeiro","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Calôba, Luiz Pereira"],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-09","date_published":"2017-09","updated_at":"2026-07-24T01:16:21Z","subjects":["Engenharia elétrica","Redes Neurais Artificiais","Abordagem de superpopulação"],"languages":["por"],"rights":["Acesso Aberto"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11422/6335","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Calôba, Luiz Pereira"]},{"key":"dc:creator","label":"Author","values":["Pereira, Savano Sousa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-02-01T15:36:48Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-05-16T03:01:24Z"]},{"key":"dc:date.issued","label":"Date","values":["2017-09"]},{"key":"dc:publisher","label":"Institution","values":["Universidade Federal do Rio de Janeiro"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia"]},{"key":"dc:type","label":"Dc Type","values":["Tese"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engenharia elétrica","Redes Neurais Artificiais","Abordagem de superpopulação"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["por"]},{"key":"dc:rights","label":"Dc Rights","values":["Acesso Aberto"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11422/6335"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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)."]},{"key":"dc:title","label":"Title","values":["Análise de dados amostrais complexos utilizando redes neurais"]}]}],"canonical_facts":{"dc:contributor.advisor":["Calôba, Luiz Pereira"],"dc:creator":["Pereira, Savano Sousa"],"dc:date.accessioned":["2019-02-01T15:36:48Z"],"dc:date.available":["2026-05-16T03:01:24Z"],"dc:date.issued":["2017-09"],"dc:description.abstract":["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)."],"dc:identifier.uri":["http://hdl.handle.net/11422/6335"],"dc:language":["por"],"dc:publisher":["Universidade Federal do Rio de Janeiro"],"dc:publisher.department":["Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia"],"dc:rights":["Acesso Aberto"],"dc:subject":["Engenharia elétrica","Redes Neurais Artificiais","Abordagem de superpopulação"],"dc:title":["Análise de dados amostrais complexos utilizando redes neurais"],"dc:type":["Tese"]},"updated_at":"2026-07-24T01:16:21Z"}