Multiple Imputation for Complex Data Sets
Multiple Imputation for Complex Data Sets
复制标题
复杂数据集的多重插补
DOI：
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复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Daniel Salfrán Vaquero
中科院分区：
文献类型：
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作者：
Daniel Salfrán Vaquero
数据分析是所有经验科学的共性，通常需要完整的数据集，但现实世界的数据收集通常会导致一些值不被观察到。当数据集不完整时，已经提出了许多复杂程度不同的补偿方法来执行统计推断，从简单的特别方法到具有精炼数学基础的方法。鉴于技术的多样性，实际研究中的问题是应用哪种技术。本文是对前人提出的一种基于位置、尺度和形状广义加法模型的推算方法的扩展。本投稿的第一章将介绍理解多重归责领域所需的基本定义。然后讨论了对GAMLSS初始工作的改进和修改。此外，还包括为提供结果而发布的软件包的快速指南。设计并执行了一项广泛的模拟研究，扩大了最新发表的关于GAMLSS推算的结果的范围。该模拟研究综合比较了多种补偿方法。
Data analysis, common to all empirical sciences, often requires complete data sets, but real-world data collection will usually result in some values being not observed. Many methods of compensation with varying degrees of complexity have been proposed to perform statistical inference when the data set is incomplete, ranging from simple ad hoc methods to approaches with refined mathematical foundation. Given the variety of techniques, the question in practical research is which one to apply. This dissertation serves to expand on a previous proposal of an imputation method based on Generalized Additive Models for Location, Scale, and Shape. The first chapters of the current contribution will present the basic definitions required to understand the Multiple Imputation field. Then the work discusses the advances and modifications made to the initial work on GAMLSS imputation. A quick guide to a software package that was published to make available the results is also included. An extensive simulation study was designed and executed expanding the scope of the latest published results concerning GAMLSS imputation. The simulation study incorporates a comprehensive comparison of multiple imputation methods.
贝叶斯推理和参数引导程序。
DOI：
10.1214/12-aoas571
发表时间：
2012-10-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Efron B
通讯作者：
Efron B
DOI：
10.1080/03610918.2014.911894
发表时间：
2016-01-01
影响因子：
0.9
作者：
De Jong, Roel;Van Buuren, Stef;Spiess, Martin
通讯作者：
Spiess, Martin