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Missing data imputation in multivariate data by evolutionary algorithms
(Computers in Human Behavior, 2011-09)
This paper presents a proposal based on an evolutionary algorithm to impute missing observations in multivariate data. A genetic algorithm based on the minimization of an error function derived from their covariance matrix ...
Estimation of Spatial Lag Model Under Random Missing Data in the Dependent Variable. Two Stage Estimator with Imputation
(Pontificia Universidad Católica del Perú, 2021)
On the influence of imputation in classification: practical issues
(TAYLOR & FRANCIS LTD, 2009)
The substitution of missing values, also called imputation, is an important data preparation task for many domains. Ideally, the substitution of missing values should not insert biases into the dataset. This aspect has ...
Estimation of Spatial Lag Model Under Random Missing Data in the Dependent Variable. Two Stage Estimator with Imputation
(Pontificia Universidad Católica del PerúPE, 2022)
Estimation of Spatial Lag Model Under Random Missing Data in the Dependent Variable. Two Stage Estimator with Imputation
(Pontificia Universidad Católica del Perú. Fondo EditorialPE, 2021)
Handling missing values in trait data
(Wiley Blackwell Publishing, Inc, 2021-01)
Aim: Trait data are widely used in ecological and evolutionary phylogenetic comparative studies, but often values are not available for all species of interest. Traditionally, researchers have excluded species without data ...
Combining multiple imputation and control function methods to deal with missing data and endogeneity in discrete-choice models
(Elsevier, 2020)
While collecting data for estimating discrete-choice models, researchers often encounter missing information in observations. In addition, endogeneity can occur whenever the error term is not independent of the observed ...
Use of Data Mining for Intelligent Evaluation of Imputation Methods
In real-world situations, researchers frequently face the difficulty of missing values (MV), i.e., values not observed in a data set. Data imputation techniques allow the estimation of MV using different algorithms, by ...
Use of data imputation tools to reconstruct incomplete air quality datasets: A case-study in Temuco, Chile
(Elsevier Ltd, 2019)
© 2018 Elsevier LtdMissing data from air quality datasets is a common problem, but is much more severe in small cities or localities. This poses a great challenge for environmental epidemiology as high exposures to pollutants ...