Results 41 to 50 of about 290,361 (291)

A Comparative Study on Missing Value Imputation Techniques in Machine Learning [PDF]

SHS Web of Conferences
Handling missing values is a crucial step in data preprocessing, as incomplete data can significantly impact model performance and overall data integrity.
Meng Haoyu
doaj   +1 more source

Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness

Journal of Causal Inference, 2020
The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when ...
Corder Nathan, Yang Shu
doaj   +1 more source

Multiple imputation of maritime search and rescue data at multiple missing patterns.

PLoS ONE, 2021
Based on the missing situation and actual needs of maritime search and rescue data, multiple imputation methods were used to construct complete data sets under different missing patterns.
Guobo Wang, Minglu Ma, Lili Jiang, Fengyun Chen, Liansheng Xu   +4 more
doaj   +1 more source

Local multiple imputation [PDF]

Biometrika, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
AERTS, Marc, CLAESKENS, Gerda, HENS, Niel, MOLENBERGHS, Geert   +3 more
openaire   +2 more sources

Missing Data Approaches in eHealth Research: Simulation Study and a Tutorial for Nonmathematically Inclined Researchers

Journal of Medical Internet Research, 2010
BackgroundMissing data is a common nuisance in eHealth research: it is hard to prevent and may invalidate research findings. ObjectiveIn this paper several statistical approaches to data “missingness” are discussed and tested in a simulation ...
Blankers, Matthijs, Koeter, Maarten W J, Schippers, Gerard M   +2 more
doaj   +1 more source

Multiple imputation for continuous variables using a Bayesian principal component analysis

, 2015
We propose a multiple imputation method based on principal component analysis (PCA) to deal with incomplete continuous data. To reflect the uncertainty of the parameters from one imputation to the next, we use a Bayesian treatment of the PCA model. Using
Audigier, Vincent, Husson, François, Josse, Julie   +2 more
core   +3 more sources

Detection of circulating tumor DNA in colorectal cancer patients using a methylation‐specific droplet digital PCR multiplex

Molecular Oncology, EarlyView.
We developed a cost‐effective methylation‐specific droplet digital PCR multiplex assay containing tissue‐conserved and tumor‐specific methylation markers. The assay can detect circulating tumor DNA with high accuracy in patients with localized and metastatic colorectal cancer.
Luisa Matos do Canto, Louise Raunkilde, Jan Lindebjerg, Mads Malik Aagaard, Christina Therkildsen, Jakob Kleif, Lars Henrik Jensen, Torben Frøstrup Hansen, Rikke Fredslund Andersen   +8 more
wiley   +1 more source

Next‐generation proteomics improves lung cancer risk prediction

Molecular Oncology, EarlyView.
This is one of very few studies that used prediagnostic blood samples from participants of two large population‐based cohorts. We identified, evaluated, and validated an innovative protein marker model that outperformed an established risk prediction model and criteria employed by low‐dose computed tomography in lung cancer screening trials.
Megha Bhardwaj, Clara Frick, Ben Schöttker, Bernd Holleczek, Hermann Brenner   +4 more
wiley   +1 more source

Imputation strategies when a continuous outcome is to be dichotomized for responder analysis: a simulation study

BMC Medical Research Methodology, 2019
Background In many clinical trials continuous outcomes are dichotomized to compare proportions of patients who respond. A common and recommended approach to handling missing data in responder analysis is to impute as non-responders, despite known biases.
Lysbeth Floden, Melanie L. Bell
doaj   +1 more source

A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data

BMC Bioinformatics, 2020
Background Single-cell RNA sequencing (scRNA-seq) provides an effective tool to investigate the transcriptomic characteristics at the single-cell resolution.
Yang Qi, Yang Guo, Huixin Jiao, Xuequn Shang   +3 more
doaj   +1 more source