Results 21 to 30 of about 1,714,069 (326)
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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Regularized approach for data missing not at random [PDF]
Statistical Methods in Medical Research, 2017 It is common in longitudinal studies that missing data occur due to subjects’ no response, missed visits, dropout, death or other reasons during the course of study. To perform valid analysis in this setting, data missing not at random (MNAR) have to be considered.
Chi-Hong, Tseng, Yi-Hau, Chen
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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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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
doaj +1 more source
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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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
doaj +1 more source
Recursive partitioning for monotone missing at random longitudinal markers. [PDF]
Stat Med, 2013 The development of HIV resistance mutations reduces the efficacy of specific antiretroviral drugs used to treat HIV infection and cross‐resistance within classes of drugs is common. Recursive partitioning has been extensively used to identify resistance mutations associated with a reduced virologic response measured at a single time point; here we ...
Stock S, DeGruttola V.
europepmc +4 more sources