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Multiple Imputation After 18+ Years [PDF]
Journal of the American Statistical Association, 1996 Abstract Multiple imputation was designed to handle the problem of missing data in public-use data bases where the data-base constructor and the ultimate user are distinct entities. The objective is valid frequency inference for ultimate users who in general have access only to complete-data software and possess limited knowledge of specific reasons ...
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Multiple Imputation of Missing Values [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2004 Following the seminal publications of Rubin about thirty years ago, statisticians have become increasingly aware of the inadequacy of “complete-case” analysis of datasets with missing observations. In medicine, for example, observations may be missing in a sporadic way for different covariates, and a complete-case analysis may omit as many as half of ...
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Investigation of the Multiple Imputation Method in Different Missing Ratios and Sample Sizes
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2019 In many studies, missing data are thereal trouble to researchers. Because the statistical methods are designed forcomplete data sets. Multiple imputation method is developed to solve themissing data problem.
Nesrin Alkan, B. Baris Alkan
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Multiple imputation in quantile regression
Biometrika, 2012 We propose a multiple imputation estimator for parameter estimation in a quantile regression model when some covariates are missing at random. The estimation procedure fully utilizes the entire dataset to achieve increased efficiency, and the resulting coefficient estimators are root-n consistent and asymptotically normal.
Ying Wei, Yanyuan Ma, Raymond J. Carroll
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Representative Wealth Data for Germany from the German SOEP: The Impact of Methodological Decisions around Imputation and the Choice of the Aggregation Unit [PDF]
The definition and operationalization of wealth information in population surveys and the corresponding microdata requires a wide range of more or less normative assumptions. However, the decisions made in both the pre- and post-data-collection stage may
Eva M. Sierminska +2 more
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Performance Comparison of Recent Imputation Methods for Classification Tasks over Binary Data
Applied Artificial Intelligence, 2017 This paper evaluates the effect on the predictive accuracy of different models of two recently proposed imputation methods, namely missForest (MF) and Multiple Imputation based on Expectation-Maximization (MIEM), along with two other imputation methods ...
Soroosh Ghorbani, Michel C. Desmarais
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The Impact of Multiple Imputation of Coarsened Data on Estimates on the Working Poor in South Africa [PDF]
South African household surveys typically contain coarsened earnings data, which consist of a mixture of missing earnings values, point responses and interval-censored responses.
Vermaark, Claire
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ACCOUNTING FOR MONOTONE ATTRITION IN A POSTPARTUM DEPRESSION CLINICAL TRIAL [PDF]
, 2006 Longitudinal studies in public health, medicine and the social sciences are often complicated by monotone attrition, where a participant drops out before the end of the study and all his/her subsequent measurements are missing.
Roumani, Yazan
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Asymptotic Results for Multiple Imputation
The Annals of Statistics, 1988 For analyzing incomplete data in surveys, \textit{D. B. Rubin} [ibid. 6, 34- 58 (1978; Zbl 0383.62021)] proposed multiple-imputation where the missing data are replaced by two or more values representing a distribution of the possible values. These studies assume that the outcome variable Y is a scalar, sample selection is by simple random sampling ...
Schenker, Nathaniel, Welsh, A. H.
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Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
, 2018 Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery ...
Cardoso, M. Jorge +6 more
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