Results 11 to 20 of about 24,713,244 (334)

To Impute or not to Impute? Missing Data in Treatment Effect Estimation

CoRR, 2022
Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missingness a particularly tricky endeavour. A key reason for this is that standard assumptions on missingness are rendered insufficient due to the presence of an additional ...
Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar   +4 more
openaire   +4 more sources

A comparison of imputation methods for categorical data

Informatics in Medicine Unlocked, 2023
Objectives: Missing data is commonplace in clinical databases, which are being increasingly used for research. Without giving any regard to missing data, results from analysis may become biased and unrepresentative.
Shaheen MZ. Memon, Robert Wamala, Ignace H. Kabano   +2 more
doaj   +1 more source

SICE: an improved missing data imputation technique

Journal of Big Data, 2020
In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these ...
Shahidul Islam Khan, Abu Sayed Md Latiful Hoque   +1 more
doaj   +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

Missing Value Imputation Using Contemporary Computer Capabilities: An Application to Financial Statements Data in Large Panels [PDF]

Economic and Business Review, 2017
This paper addresses an evaluation of the methods for automatic item imputation to large datasets with missing data in the particular setting of financial data often used in economic and business settings.
Ales Gorisek, Marko Pahor
doaj   +1 more source

Informer-WGAN: High Missing Rate Time Series Imputation Based on Adversarial Training and a Self-Attention Mechanism

Algorithms, 2022
Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis.
Yufan Qian, Limei Tian, Baichen Zhai, Shufan Zhang, Rui Wu   +4 more
doaj   +1 more source

A Systematic Literature Review On Missing Values: Research Trends, Datasets, Methods and Frameworks [PDF]

E3S Web of Conferences, 2023
Handling of missing values in data analysis is the focus of attention in various research fields. Imputation is one method that is commonly used to overcome this problem of missing data.
Setiawan Ismail, Gernowo Rahmat, Warsito Budi   +2 more
doaj   +1 more source

A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasets

Comput. Biol. Medicine, 2023
The missing data mechanism is a relevant problem in Machine Learning (ML) and biomedical informatics communities. Real-world Electronic Health Record (EHR) datasets comprise several missing values, thus revealing a high level of spatiotemporal sparsity ...
Michele Bernardini, Anastasiia Doinychko, L. Romeo, E. Frontoni, Massih-Reza Amini   +4 more
semanticscholar   +1 more source

Methods to Handle Incomplete Data

MAMC Journal of Medical Sciences, 2020
Context: The major question for data analysis is determining the appropriate analytic approach in the presence of incomplete observations. The most common solution to handle missing data in a data set is imputation, where missing values are estimated and
Vinny Johny, Mariamma Philip, Swathi Augustine   +2 more
doaj   +1 more source

IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data

IEEE Access, 2021
Increasing use of sensor data in intelligent transportation systems calls for accurate imputation algorithms that can enable reliable traffic management in the occasional absence of data.
Amir Kazemi, Hadi Meidani
doaj   +1 more source