Journal ArticleDOI
The use of multiple imputation for the analysis of missing data.
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TLDR
The idea behind MI, the advantages of MI over existing techniques for addressing missing data, how to do MI for real problems, the software available to implement MI, and the results of a simulation study aimed at finding out how assumptions regarding the imputation model affect the parameter estimates provided by MI are discussed.Abstract:
This article provides a comprehensive review of multiple imputation (MI), a technique for analyzing data sets with missing values. Formally, MI is the process of replacing each missing data point with a set of m > 1 plausible values to generate m complete data sets. These complete data sets are then analyzed by standard statistical software, and the results combined, to give parameter estimates and standard errors that take into account the uncertainty due to the missing data values. This article introduces the idea behind MI, discusses the advantages of MI over existing techniques for addressing missing data, describes how to do MI for real problems, reviews the software available to implement MI, and discusses the results of a simulation study aimed at finding out how assumptions regarding the imputation model affect the parameter estimates provided by MI.read more
Citations
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Structurally missing data problems in multiple list capture-recapture data
TL;DR: In this paper, two problems are studied and solved using a common solution using the EM algorithm, and a solution is obtained through the missing data problem, which is illustrated by two examples.
Journal ArticleDOI
EvoImputer: An evolutionary approach for Missing Data Imputation and feature selection in the context of supervised learning
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A New Method for Estimation of Missing Data Based on Sampling Methods for Data Mining
TL;DR: Today the authors collect large amounts of data and they receive more than they can handle, the accumulated data are often raw and far from being of good quality they contain Missing Values and noise.
Journal ArticleDOI
EvoImputer: An evolutionary approach for Missing Data Imputation and feature selection in the context of supervised learning
TL;DR: In this article , the authors used evolutionary algorithms to evaluate the usefulness of the imputation for each feature on the performance of the prediction model, in order to select the best subset of incomplete features that can enhance the learning process and maximize the prediction power after it has been handled properly.
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Innovation Implementation Effectiveness: A Mutiorganizational Test of Klein Con and Sorra's Model
Sukanlaya Sawang,Kerrie Unsworth +1 more
TL;DR: In this article, an empirical test and extension of Klein Conn and Sorra's model of innovation implementation effectiveness was conducted to identify the generalizability of their data-modified model in comparison with their theorised model.
References
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Maximum likelihood from incomplete data via the EM algorithm
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Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
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TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
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Multiple imputation for nonresponse in surveys
TL;DR: In this article, a survey of drinking behavior among men of retirement age was conducted and the results showed that the majority of the participants reported that they did not receive any benefits from the Social Security Administration.
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Analysis of Incomplete Multivariate Data
TL;DR: The Normal Model Methods for Categorical Data Loglinear Models Methods for Mixed Data and Inference by Data Augmentation Methods for Normal Data provide insights into the construction of categorical and mixed data models.