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Missing observations in stochastic difference equation with arma errorsMissing observations in stochastic difference equation with arma errors
(Sociedade Brasileira de Econometria, 1987)
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 ...
Robust doubly protected estimators for quantiles with missing data
(Springer, 2020-09)
Doubly protected methods are widely used for estimating the population mean of an outcome Y from a sample where the response is missing in some individuals. To compensate for the missing responses, a vector X of covariates ...
High breakdown point robust estimators with missing data
(Taylor, 2018-11)
In this paper, we propose a new procedure to estimate the distribution of a variable y when there are missing data. To compensate the presence of missing responses, it is assumed that a covariate vector x is observed and ...
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 ...
Biases with the generalized euclidean distance measure in disparity analyses with high levels of missing data
(Wiley Blackwell Publishing, Inc, 2019-05)
The Generalized Euclidean Distance (GED) measure has been extensively used to conduct morphological disparity analyses based on palaeontological matrices of discrete characters. This is in part because some implementations ...
Estimating additive models with missing responses
(Taylor & Francis, 2016-01)
For multivariate regressors, the Nadaraya-Watson regression estimator suffers from the well-known curse of dimensionality. Additive models overcome this drawback. To estimate the additive components, it is usually assumed ...
Missing-value imputation using the robust singular-value decomposition: Proposals and numerical evaluationImputación de valores faltantes usando la descomposición robusta en valores singulares: Propuestas y evaluación numérica
(Crop Science, 26/03/2021)
A common problem in the analysis of data from multi-environment trials is imbalance caused by missing observations. To get around this problem, Yan proposed a method for imputing the missing values based on the singular-value ...
Abordagem do near miss neonatal no 2005 WHO Global Survey BrazilNeonatal near miss approach in the 2005 WHO Global Survey Brazil
(Sociedade Brasileira de Pediatria, 2010)