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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 ...
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 ...
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 ...
Comparing diagnostic tests with missing data
(ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD, 2011)
When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive ...
Robust tests in generalized linear models with missing responses
(Elsevier Science Bv, 2013-09)
In many situations, data follow a generalized linear model in which the mean of the responses is modelled, through a link function, linearly on the covariates. Robust estimators for the regression parameter in order to ...
Dealing with Missing Data using a Selection Algorithm on Rough Sets
(ATLANTIS PRESS, 2018-01-01)
This paper discusses the so-called missing data problem, i.e. the problem of imputing missing values in information systems. A new algorithm, called the ARSI algorithm, is proposed to address the imputation problem of ...
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 ...