A bias-corrected estimator in multiple imputation for missing data-Reference-Cited by-同舟云学术

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A bias-corrected estimator in multiple imputation for missing data

Published:2018-05-29 Issue:23 Volume:37 Page:3373-3386
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statistics in Medicine

Author:

Tomita Hiroaki¹^ORCID,Fujisawa Hironori¹²³,Henmi Masayuki¹²

Affiliation:

1. Department of Statistical Science, School of Multidisciplinary Sciences; SOKENDAI (The Graduate University for Advanced Studies); Tokyo Japan

2. The Institute of Statistical Mathematics; Tokyo Japan

3. Department of Mathematical Statistics; Nagoya University Graduate School of Medicine; Nagoya Japan

Funder

Project Promoting Clinical Trials for Development of New Drugs

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/sim.7833/fullpdf

Reference44 articles.

1. Multiple Imputation for Nonresponse in Surveys

2. Multiple Imputation and its Application

3. The use and reporting of multiple imputation in medical research-a review;Mackinnon;J Intern Med,2010

4. Large-sample theory for parametric multiple imputation procedures;Wang;Biometrika,1998

5. Inference for imputation estimators;Robins;Biometrika,2000

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1. Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review;BMC Medical Research Methodology;2024-08-28

2. An effective ensemble method for missing data imputation;International Journal of Information and Computer Security;2023

3. Weighted multiple blockwise imputation method for high-dimensional regression with blockwise missing data;Journal of Statistical Computation and Simulation;2022-08-24

4. Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset;Entropy;2022-02-16

5. Diagnostic checking of multiple imputation models;AStA Advances in Statistical Analysis;2022-01-14

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