Results 21 to 30 of about 762,527 (191)

A comparison of two approaches to implementing propensity score methods following multiple imputation

Epidemiology, Biostatistics and Public Health, 2022
Background. In observational research on causal effects, missing data and confounding are very common problems. Multiple imputation and propensity score methods have gained increasing interest as methods to deal with these, but despite their ...
Bas BL Penning de Vries, Rolf HH Groenwold   +1 more
doaj   +1 more source

Variational Bayesian Inference for Quantile Regression Models with Nonignorable Missing Data

Mathematics, 2023
Quantile regression models are remarkable structures for conducting regression analyses when the data are subject to missingness. Missing values occur because of various factors like missing completely at random, missing at random, or missing not at ...
Xiaoning Li, Mulati Tuerde, Xijian Hu
doaj   +1 more source

Deep Generative Imputation Model for Missing Not At Random Data [PDF]

International Conference on Information and Knowledge Management, 2023
Data analysis usually suffers from the Missing Not At Random (MNAR) problem, where the cause of the value missing is not fully observed. Compared to the naive Missing Completely At Random (MCAR) problem, it is more in line with the realistic scenario ...
Jia-Lve Chen, Yuanbo Xu, Pengyang Wang, Yongjian Yang   +3 more
semanticscholar   +1 more source

Multiple Imputation Ensembles (MIE) for dealing with missing data [PDF]

, 2020
Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation ...
A Farhangfar, AM Sefidian, B Schölkopf, C Cortes, CT Tran, DA Newman, DB Rubin, DB Rubin, DH Wolpert, EL Silva-Ramírez, GE Batista, GJ van der Heijden, H Gao, IH Witten, J Demšar, J Honaker, J Honaker, J Scheffer, JA Sterne, JL Schafer, JL Schafer, JR Quinlan, K Abayomi, KM Ting, L Breiman, L Breiman, L Rokach, M Fichman, M Khalilia, M Spratt, MA Klebanoff, MJ Azur, NJ Horton, PJ García-Laencina, PJ Kelly, PN Tan, RJ Little, S García, S Van Buuren, S Van Buuren, SS Chae, SS Choi, U Garciarena, V Vapnik, X Chen, Y Dong, Y Freund, Y He, Z Che, Z Liu   +49 more
core   +1 more source

Multiple imputation for propensity score analysis with covariates missing at random: some clarity on within and across methods. [PDF]

American Journal of Epidemiology, 2023
In epidemiology and social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling covariate missingness.
T. Nguyen, E. Stuart
semanticscholar   +1 more source

A simplified eye-opener on managing missing data and in evaluation of non-response bias in medical research: a narrative review

Journal of the College of Community Physicians, 2018
Using correct methods for prevention, analysis and treatment of missing data is essential in preserving the validity of scientific research. In spite of this, issues related to missing data and non-response bias are found to be inadequately discussed in ...
P. K. B. Mahesh, Wasantha Gunathunga, Mahendra arnold, S. B. Munasinghe, Sinha De Silva   +4 more
doaj   +1 more source

Evaluation of Multiple Imputation with Large Proportions of Missing Data: How Much Is Too Much?

Iranian Journal of Public Health, 2021
Background: Multiple Imputation (MI) is known as an effective method for handling missing data in public health research. However, it is not clear that the method will be effective when the data contain a high percentage of missing observations on a ...
Jin Hyuk Lee, J. Charles Huber Jr.
doaj   +1 more source

Methylation data imputation performances under different representations and missingness patterns

BMC Bioinformatics, 2020
Background High-throughput technologies enable the cost-effective collection and analysis of DNA methylation data throughout the human genome. This naturally entails missing values management that can complicate the analysis of the data.
Pietro Di Lena, Claudia Sala, Andrea Prodi, Christine Nardini   +3 more
doaj   +1 more source

A survey on missing data in machine learning

Journal of Big Data, 2021
Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or ...
Tlamelo Emmanuel, Thabiso Maupong, Dimane Mpoeleng, Thabo Semong, Banyatsang Mphago, Oteng Tabona   +5 more
doaj   +1 more source

Power difference in a χ2 test vs generalized linear mixed model in the presence of missing data – a simulation study

BMC Medical Research Methodology, 2020
Background Longitudinal randomized controlled trials (RCTs) often aim to test and measure the effect of treatment between arms at a single time point. A two-sample χ2 test is a common statistical approach when outcome data are binary.
Mary L. Miller, Denise J. Roe, Chengcheng Hu, Melanie L. Bell   +3 more
doaj   +1 more source