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Testing Missing at Random Using Instrumental Variables [PDF]
Journal of Business & Economic Statistics, 2017 This paper proposes a test for missing at random (MAR). The MAR assumption is shown to be testable given instrumental variables which are independent of response given potential outcomes. A nonparametric testing procedure based on integrated squared distance is proposed. The statistic's asymptotic distribution under the MAR hypothesis is derived.
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Semiparametric Regression Analysis With Missing Response at Random [PDF]
Journal of the American Statistical Association, 2003 We develop inference tools in a semiparametric partially linear regression model with missing response data. A class of estimators is defined that includes as special cases a semiparametric regression imputation estimator, a marginal average estimator, and a (marginal) propensity score weighted estimator.
Wolfgang Härdle +2 more
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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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Robust Optimal Design When Missing Data Happen at Random
Journal of Statistical Theory and Practice, 2023 In this article, we investigate the robust optimal design problem for the prediction of response when the fitted regression models are only approximately specified, and observations might be missing completely at random. The intuitive idea is as follows: We assume that data are missing at random, and the complete case analysis is applied.
Rui Hu, Ion Bica, Zhichun Zhai
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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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What Is Meant by “Missing at Random”?
Statistical Science, 2013 The concept of missing at random is central in the literature on statistical analysis with missing data. In general, inference using incomplete data should be based not only on observed data values but should also take account of the pattern of missing values.
Seaman, Shaun +3 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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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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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
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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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