Missing data: An update on the state of the art.

缺少数据 插补(统计学) 计算机科学 数据科学 软件 数据挖掘 贝叶斯概率 情报检索 计量经济学 人工智能 机器学习 数学 程序设计语言
作者
Craig K. Enders
出处
期刊:Psychological Methods [American Psychological Association]
被引量:48
标识
DOI:10.1037/met0000563
摘要

The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of Psychological Methods. Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of applications that are possible with modern missing data techniques has increased dramatically, and software options are light years ahead of where they were. This article provides an update on the state of the art that catalogs important innovations from the past two decades of missing data research. The paper addresses topics described in the original paper, including developments related to missing data theory, full information maximum likelihood, Bayesian estimation, multiple imputation, and models for missing not at random processes. The paper also describes newer factored regression specifications and missing data handling for multilevel models, both of which have been a focus of recent research. The paper concludes with a summary of the current software landscape and a discussion of several practical issues. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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