Multiple Imputation Data
Mostrando 1-10 de 10 artigos, teses e dissertações.
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1. Iniquidades étnico-raciais nas hospitalizações por causas evitáveis em menores de cinco anos no Brasil, 2009-2014
Resumo: Internacionalmente, observa-se um incremento no uso das internações por condições sensíveis à atenção primária (ICSAP) como indicador de efetividade da atenção primária à saúde. Este artigo analisa as iniquidades étnico-raciais nas internações por causas em menores de cinco anos no Brasil e regiões, com ênfase nas ICSAP e nas infec
Cad. Saúde Pública. Publicado em: 19/08/2019
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2. Missing value imputation in multi-environment trials: Reconsidering the Krzanowski method
Abstract We propose a new methodology for multiple imputation when faced with missing data in multi-environmental trials with genotype-by-environment interaction, based on the imputation system developed by Krzanowski that uses the singular value decomposition (SVD) of a matrix. Several different iterative variants are described; differential weights can als
Crop Breed. Appl. Biotechnol.. Publicado em: 2016-06
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3. Imputação múltipla: comparação e eficiência em experimentos multiambientais / Multiple Imputations: comparison and efficiency of multi-environmental trials
In trials of genotypes by environment, the presence of absent values is common, due to the quantity of insufficiency of genotype application, making difficult for example, the process of recommendation of more productive genotypes, because for the application of the majority of the multivariate statistical techniques, a complete data matrix is required. Thus
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 19/07/2012
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4. Estratégias para tratamento de variáveis com dados faltantes durante o desenvolvimento de modelos preditivos / Strategies for treatment of variables with missing data during the development of predictive models
Predictive models have been increasingly used by the market in order to assist companies in risk mitigation, portfolio growth, customer retention, fraud prevention, among others. During the model development, however, it is usual to have, among the predictive variables, some who have data not filled in (missing values), thus it is necessary to adopt a proced
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 09/05/2012
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5. Tratamento de dados faltantes empregando biclusterização com imputação múltipla / Treatment of missing data using biclustering with multiple imputation
The answers provided by recommender systems can be interpreted as missing data to be imputed considering the knowledge associated with the available data and the relation between the available and the missing data. There is a wide range of techniques for data imputation, and this work is concerned with multiple imputation. Alternative approaches for multiple
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 22/06/2011
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6. Métodos de imputação de dados aplicados na área da saúde
Em pesquisas da área da saúde é muito comum que o pesquisador defronte-se com o problema de dados faltantes. Nessa situação, é freqüente que a decisão do pesquisador seja desconsiderar os sujeitos que tenham não-resposta em alguma ou algumas das variáveis, pois muitas das técnicas estatísticas foram desenvolvidas para analisar dados completos. En
Publicado em: 2007
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7. UM ALGORITMO - EM - PARA IMPUTAÇÃO MÚLTIPLA DE DADOS CENSURADOS / MULTIPLE IMPUTATION IN MULTIVARIATE NORMAL DATA VIA A EM TYPE ALGORITHM
An EM algorithm was developed to parameter estimation of a multivariate truncate normal distribution. The multiple imputation is evaluated by the conditional expectation becoming the estimated values greater or lower than the observed value. The information matrix gives the confident interval to the parameter and values estimations. The proposed algorithm wa
Publicado em: 2002
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8. Multiple Imputation With Large Data Sets: A Case Study of the Children's Mental Health Initiative
Multiple imputation is an effective method for dealing with missing data, and it is becoming increasingly common in many fields. However, the method is still relatively rarely used in epidemiology, perhaps in part because relatively few studies have looked at practical questions about how to implement multiple imputation in large data sets used for diverse p
Oxford University Press.
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9. Multiple imputation for missing cardiac magnetic resonance imaging data: Results from the Multi-Ethnic Study of Atherosclerosis (MESA)
Pulsus Group Inc.
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10. Urinary Protein Profiles in a Rat Model for Diabetic Complications*
Diabetes mellitus is estimated to affect ∼24 million people in the United States and more than 150 million people worldwide. There are numerous end organ complications of diabetes, the onset of which can be delayed by early diagnosis and treatment. Although assays for diabetes are well founded, tests for its complications lack sufficient specificity and se
The American Society for Biochemistry and Molecular Biology.