Results 1 to 10 of about 204,036 (213)
Transformers deep learning models for missing data imputation: an application of the ReMasker model on a psychometric scale [PDF]
Frontiers in PsychologyIntroductionMissing data in psychometric research presents a substantial challenge, impacting the reliability and validity of study outcomes. Various factors contribute to this issue, including participant non-response, dropout, or technical errors ...
Monica Casella +3 more
doaj +2 more sources
A Benchmark for Data Imputation Methods [PDF]
Frontiers in Big Data, 2021 With the increasing importance and complexity of data pipelines, data quality became one of the key challenges in modern software applications. The importance of data quality has been recognized beyond the field of data engineering and database management systems (DBMSs).
Sebastian Jäger +2 more
openaire +3 more sources
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 +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 +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 +1 more
doaj +1 more source
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 +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 +2 more
doaj +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
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
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 +2 more
doaj +1 more source