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Estimation of Spatial Lag Model Under Random Missing Data in the Dependent Variable. Two Stage Estimator with Imputation
(Pontificia Universidad Católica del Perú, 2021)
Asymptotic behavior of robust estimators in partially linear models with missing responses: The effect of estimating the missing probability on the simplified marginal estimators
(Springer, 2011-11)
In this paper, we consider a semiparametric partially linear regression model where missing data occur in the response. We derive the asymptotic behavior of the robust estimators for the regression parameter and of the ...
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)
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 ...
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 mechanisms and their implications on the analysis of categorical data
(SPRINGER, 2011)
We review some issues related to the implications of different missing data mechanisms on statistical inference for contingency tables and consider simulation studies to compare the results obtained under such models to ...
Robust location estimators in regression models with covariates and responses missing at random
(Taylor & Francis Ltd, 2020-11-04)
This paper deals with robust marginal estimation under a general regression model when missing data occur in the response and also in some covariates. The target is a marginal location parameter given through an M-functional. ...
Mean estimation with data missing at random for functional covariables
(Taylor & Francis, 2013-08)
In a missing-data setting, we want to estimate the mean of a scalar outcome, based on a sample in which an explanatory variable is observed for every subject while responses are missing by happenstance for some of them. ...
Robust inference in partially linear models with missing responses
(Elsevier Science, 2014-11)
We consider robust testing on the regression parameter of a partially linear regression model, where missing responses are allowed. We derive the asymptotic behavior of the proposed test statistic under the null and ...