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Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge
Entropy, 2022 In this work, we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data.
Maximilian Kertel, Markus Pauly
doaj +1 more source
Estimating linear functionals in nonlinear regression with responses missing at random [PDF]
, 2009 We consider regression models with parametric (linear or nonlinear) regression function and allow responses to be ``missing at random.'' We assume that the errors have mean zero and are independent of the covariates.
Müller, Ursula U.
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Biased Estimation of Adjusted Odds Ratios From Incomplete Covariate Data Due to Violation of the Missing at Random Assumption [PDF]
, 1996 We investigate the possible bias due to an erroneous missing at random assumption if adjusted odds ratios are estimated from incomplete covariate data using the maximum likelihood principle.
Illi, S., Vach, W.
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Regression-Based Approach to Test Missing Data Mechanisms
Data, 2022 Missing data occur in almost all surveys; in order to handle them correctly it is essential to know their type. Missing data are generally divided into three types (or generating mechanisms): missing completely at random, missing at random, and missing ...
Serguei Rouzinov, André Berchtold
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Large-Scale Expectile Regression With Covariates Missing at Random
IEEE Access, 2020 Analysis of large volumes of data is very complex due to not only a high level of skewness and heteroscedasticity of variance but also the phenomenon of missing data.
Yingli Pan, Zhan Liu, Wen Cai
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Multiple Imputation Ensembles (MIE) for dealing with missing data [PDF]
, 2020 Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation ...
A Farhangfar +49 more
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Statistical Theory and Related Fields, 2018 Feature screening with missing data is a critical problem but has not been well addressed in the literature. In this discussion we propose a new screening index based on “information value” and apply it to feature screening with missing covariates.
Fang Fang, Lyu Ni
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On using a non-probability sample for the estimation of population parameters
Lietuvos Matematikos Rinkinys, 2023 We aim to find a way to effectively integrate a non-probability (voluntary) sample under the data framework, where the study variable is also observed in a probability sample of some statistical survey.
Ieva Burakauskaitė, Andrius Čiginas
doaj +3 more sources
On Testing the Missing at Random Assumption [PDF]
, 2006 Most approaches to learning from incomplete data are based on the assumption that unobserved values are missing at random (mar). While the mar assumption, as such, is not testable, it can become testable in the context of other distributional assumptions, e.g. the naive Bayes assumption.
openaire +3 more sources
BMC Medical Research Methodology, 2019 Background The benefit of a given treatment can be evaluated via a randomized clinical trial design. However, protocol deviations may severely compromise treatment effect since such deviations often lead to missing values.
Abdul-Karim Iddrisu, Freedom Gumedze
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